(SAR) to Support Agricultural Monitoring 2 - Group on Earth ...

International GEO Workshop on Synthetic Aperture Radar (<strong>SAR</strong>) 

<strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring 

2 – 4 November 2009 

Kananaskis, Alberta, Canada 

Workshop Report 

© Government of Canada, 2010

International GEO Workshop on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Executive Summary 

The Government of Canada, through the members of the interdepartmental Canadian <strong>Group</strong> 

on Earth Observations (CGEO), coordinated with the international GEO Global <strong>Agricultural</strong> 

Moni<strong>to</strong>ring Community of Practice and other sponsors and partners, <strong>to</strong> organize and host the 

International GEO Workshop on Synthetic Aperture Radar (<strong>SAR</strong>) <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> 

Moni<strong>to</strong>ring, which was held in Kananaskis, Alberta, Canada from 2-4 November 2009. The 

workshop was preceded by a pre-workshop “by invitation” capacity building training session 

from 31 Oc<strong>to</strong>ber – 1 November. 

This workshop brought <strong>to</strong>gether 115 participants from around the world, with some 75 of the 

experts also participating in the pre-workshop capacity building training session. Pre-training 

and post-workshop field trips were conducted by the University of Calgary and Parks 

Canada, and a planning meeting of the GEO Global <strong>Agricultural</strong> Moni<strong>to</strong>ring Community of 

Practice was held on 5 November. The participants included experts and managers who map 

agricultural land cover and land use; moni<strong>to</strong>r changes in the agricultural land base; identify 

crops and estimate crop area; qualitatively and quantitatively assess crop condition; and 

moni<strong>to</strong>r properties related <strong>to</strong> water availability such as soil moisture. 

Funding from the Canadian International Development Agency (CIDA) provided travel and 

related expenses for 29 participants from developing countries <strong>to</strong> actively participate in the 

training course, workshop and field trips. Additional persons were sponsored by GOFC- 

GOLD (Global Observation of Forest and Land Cover Dynamics), Agriculture and Agri- 

Food Canada, and Environment Canada. 

The objectives of the workshop were <strong>to</strong> increase understanding and capacity <strong>to</strong> use <strong>SAR</strong> data 

for agricultural moni<strong>to</strong>ring, and the identify the needs of the agricultural community <strong>to</strong> space 

agencies and others involved in the delivery of <strong>SAR</strong> data and services. The workshop 

successfully provided a structure and forum <strong>to</strong> state clearly and strongly the Earth 

observation requirements of the international agriculture community and the strategies 

needed <strong>to</strong> promote the use of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring, in the context of science, 

research, operations and capacity. The participants also identified the information and 

operational needs of the agricultural user community <strong>to</strong> facilitate coordinated access <strong>to</strong> Earth 

observation data. 

The breakout groups and the panel discussion identified a number of frequently recurring 

priorities and recommendations, including the need <strong>to</strong>: 

1. Organize a <strong>SAR</strong> component of the GEO Global <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Community of Practice (CoP) and its partners <strong>to</strong> coordinate initiatives related <strong>to</strong> 

the development of the <strong>Agricultural</strong> Moni<strong>to</strong>ring System of Systems. 

2. Pressure space agencies and data providers for lower cost <strong>SAR</strong> data and <strong>to</strong> 

remove restrictions on sharing and copying of data. 

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3. Use JECAM (GEO Joint Experiment for Crop Area Moni<strong>to</strong>ring: Towards 

an <strong>Agricultural</strong> Moni<strong>to</strong>ring System of Systems) as a mechanism for capacity 

building in developing countries. 

4. Establish a website portal that includes training on <strong>SAR</strong> methods and best 

practices for agriculture applications, including manuals, glossary of terms, list of 

experts, user forums and links <strong>to</strong> <strong>SAR</strong> data providers and other resources. 

5. Encourage scientific exchanges, capacity building and training through GEO 

organized annual/bi-annual workshops. 

6. Establish international supersites <strong>to</strong> facilitate research collaborations. 

7. Document and clearly communicate data requirements of the agriculture 

community with respect <strong>to</strong>: spatial resolution; spatial coverage; temporal 

revisit/relook; data latency/delivery; and, <strong>SAR</strong> sensor parameters (frequency, 

polarizations, angles). These requirements should be communicated <strong>to</strong> the Space 

Agencies. 

8. Advocate the importance of data continuity policies through future satellite 

missions. 

9. Prioritize and coordinate development of training programs and 

analysis/application <strong>to</strong>ols and make these available <strong>to</strong> developing countries. 

The GEO Global <strong>Agricultural</strong> Moni<strong>to</strong>ring Community of Practice (CoP) will take the lead <strong>to</strong> 

incorporate the priorities and recommendations in<strong>to</strong> upcoming planning exercises in relation 

<strong>to</strong> the tasks and activities of the GEO work planning process. Also, through the JECAM 

initiative, the CoP will coordinate the development of a series of pilot sites around the world 

<strong>to</strong> evaluate methods for deriving information from Earth observation sensors, with an 

emphasis on <strong>SAR</strong> as a key data component of these sites. Outcomes from the workshop and 

next steps will be further elaborated as a discussion item at future CoP meetings and 

workshops in 2010. 

In addition, the peer-reviewed Canadian Journal of Remote Sensing will publish a Special 

Issue on <strong>Agricultural</strong> Moni<strong>to</strong>ring using <strong>SAR</strong> in mid-2010. Papers are solicited from 

applications of <strong>SAR</strong> and <strong>SAR</strong>/optical integration on the <strong>to</strong>pics covered by this meeting. 

The workshop presentations are available on-line at www.cgeo.gc.ca and a CD will be 

produced with the PowerPoint presentations, along with the other workshop and training 

materials and this report. The CD will be sent <strong>to</strong> all attendees as well as <strong>to</strong> others upon 

request. Additional copies will be made available at future events in which the GEO Global 

<strong>Agricultural</strong> Moni<strong>to</strong>ring Community of Practice participates. 

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Table of Contents 

Executive Summary................................................................................................................. i 

Table of Contents ………………………………………………………………………...…iii 

List of Figures ………………………………………………………………...……………iv 

List of Tables …………………………………………………………………..…………..iv 

1. Background ......................................................................................................................1 

2. Summary of Meeting Sessions ........................................................................................3 

2.1 Plenary and Keynote Addresses.................................................................................3 

2.2 <strong>SAR</strong> Data Providers...................................................................................................7 

2.3 <strong>Agricultural</strong> Productivity ...........................................................................................9 

2.4 Soil and Water Availability .......................................................................................9 

2.5 Land Cover and Land Management.........................................................................10 

2.6 Capacity Building Activities and Opportunities......................................................11 

3. Breakout Sessions...........................................................................................................15 

3.1 Findings of Pre-workshop Survey ...........................................................................15 

3.2 Scientific and Operational Requirements, Challenges and Strategies .....................17 

3.3 Transitioning from Research <strong>to</strong> Operational Use and Capacity Building ...............22 

4. Panel Discussion.............................................................................................................26 

4.1 Space Agency Summary..........................................................................................26 

4.2 Value Added Community Summary........................................................................26 

4.3 Operational <strong>Agricultural</strong> Moni<strong>to</strong>ring Community Summary ..................................26 

4.4 University Researcher Community Summary .........................................................27 

4.5 Developing Country Scientists / Users Summary....................................................27 

4.6 Overall Workshop Summary ...................................................................................27 

5. Recommendations..........................................................................................................32 

6. Actions & Next Steps .....................................................................................................33 

Appendix A – Acknowledgements, Sponsors and Partners ............................................. 34 

Appendix B – Agenda .......................................................................................................... 36 

Appendix C – Findings of Breakout Sessions …………………………………………… 42 

Appendix D – Alaska Satellite Facility: Data Portal, <strong>SAR</strong> Tools, and <strong>SAR</strong> Training ...43 

Appendix E – Workshop Organizing Committee ............................................................. 44 

Appendix F – International Technical Committee …………………………………...… 45 

Appendix G – List of Poster Presentations ……………………………………………… 46 

Appendix H – List of Participants ……………………………………………………..… 48 

Annex 1 – Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring … 49 

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List of Figures 

Figure 1 Growth Stages of a CoP - Where Are We? ...............................................................6 

Figure 2 Sample Field Campaign in Argentina .......................................................................8 

Figure 3 Scattering Mechanisms............................................................................................10 

Figure 4 RADARSAT-2 Fine Quad Mode Scene of Flevoland, Netherlands.......................12 

Figure 5 Top Twelve Applications of <strong>Agricultural</strong> Moni<strong>to</strong>ring Data....................................15 

Figure 6 Conceptual Diagram of the <strong>Agricultural</strong> Moni<strong>to</strong>ring System of Systems ..............29 

Figure 7 Transitioning Research <strong>to</strong> Operations .....................................................................30 

List of Tables 

Table 1 Recommended Approach <strong>to</strong> Breakout Sessions .......................................................16 

Table 2 Recommended Approach for Scientific and Operational Challenges ......................19 

Table 3 Recommended Approach for Transitioning <strong>to</strong> Operations and Capacity Building..23 

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1. Background 

On 16 – 18 July 2007, the second IGOL (Integrated Global Observations for Land)/ GEO 

(<strong>Group</strong> on Earth Observations) workshop convened at the headquarters of the UN Food and 

Agriculture Organization (FAO) in Rome <strong>to</strong> develop a strategy for global agricultural 

moni<strong>to</strong>ring in the framework of GEO. At that meeting, the participants representing twentyfive 

national and international organizations established the ‘GEOSS/IGOL <strong>Agricultural</strong> 

Moni<strong>to</strong>ring Community of Practice’. The workshop reviewed the current state of agricultural 

moni<strong>to</strong>ring and revised the GEO worplan <strong>Agricultural</strong> Moni<strong>to</strong>ring Task (AG-07-03). A 

strong case was made for increased attention <strong>to</strong> the emerging <strong>SAR</strong> (synthetic aperture radar) 

capabilities for agricultural moni<strong>to</strong>ring. There was a perceived need for a community 

workshop <strong>to</strong> develop a consensus on current capabilities and the future role of <strong>SAR</strong> data in 

the Ag 0703 Task. 

In response <strong>to</strong> this view, the Canadian <strong>Group</strong> on Earth Observations (CGEO) joined with the 

Government of Canada and other sponsors <strong>to</strong> organize the International GEO Workshop on 

<strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring in Kananaskis, Alberta on 2 – 4 November 2009. 

This workshop purpose was <strong>to</strong> bring <strong>to</strong>gether remote sensing experts and resource managers 

who map agricultural land cover and land use; moni<strong>to</strong>r changes in the agricultural land base; 

identify crops and estimate crop area; qualitatively and quantitatively assess crop condition; 

and moni<strong>to</strong>r properties related <strong>to</strong> water availability such as soil moisture <strong>to</strong> provide a forum 

for discussion of how <strong>to</strong> advance the use of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring. 

The workshop was organized <strong>to</strong> identify and communicate the information and operational 

needs of the agricultural user community <strong>to</strong> facilitate coordinated access <strong>to</strong> Earth observation 

data. A “by invitation” training session was organized, targeted <strong>to</strong>wards participants from 

developing countries, and it preceded the workshop from 31 Oc<strong>to</strong>ber – 1 November. 

The goal of the workshop was <strong>to</strong> state clearly and strongly the Earth Observation 

requirements of the international agriculture community and the strategies needed <strong>to</strong> promote 

the use of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring, in the context of science, research, operations and 

capacity. 

The specific objectives of the workshop were <strong>to</strong>: 

1. Increase understanding of the capabilities of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring; 

2. Share best practices with respect <strong>to</strong> processing, analysis and modeling of <strong>SAR</strong> 

data for agricultural mapping and moni<strong>to</strong>ring; 

3. Increase awareness of software <strong>to</strong>ols, applications and processes for the effective 

use of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring; 

4. Build capacity within the agricultural user community <strong>to</strong> exploit <strong>SAR</strong> data, 

especially in developing countries; and 

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5. Initiate a dialogue <strong>to</strong> identify the needs of the agriculture moni<strong>to</strong>ring community 

with <strong>SAR</strong> satellite opera<strong>to</strong>rs and <strong>SAR</strong> application software developers. 

In preparation for this workshop, a survey was sent out <strong>to</strong> the agricultural moni<strong>to</strong>ring 

community. The purpose of this survey was <strong>to</strong> collect the views of the community and 

present this <strong>to</strong> the workshop attendees, <strong>to</strong> ‘prime the pump’ and stimulate discussion for 

producing effective recommendations during the workshop. This provided the organization 

and technical team the current state of agricultural moni<strong>to</strong>ring, in particular using <strong>SAR</strong> data, 

and what the issues are and what actions could encourage greater use of <strong>SAR</strong> data for 

agricultural moni<strong>to</strong>ring. 

The workshop agenda is provided in Appendix B, the list of participants in Appendix H, the 

survey results are found in the report in Annex 1, and the list of poster presentations in 

Appendix G. The Workshop Organizing Committee and International Technical Team are 

listed in Appendices E and F. The findings of the breakout sessions are available in 

Appendix C. Information about the ASF (Alaska Satellite Facility) Data Portal, <strong>SAR</strong> Tools, 

and <strong>SAR</strong> Training is provided in Appendix D. 

The workshop presentations are available on-line at: 

http://www.cgeo.gc.ca/events-evenements/sar-ros/agenda-ordre-eng.asp 

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2. Summary of Meeting Sessions 

2.1 Plenary and Keynote Addresses 

Ken Korporal of the CGEO Secretariat opened the workshop by welcoming the participants, 

which included more than 115 experts representing 30 countries and seven Canadian 

provinces. More than 75 people participated in the weekend training session and field trips 

led by the Agriculture Canada, the University of Calgary and Parks Canada. He 

acknowledged the funding of sponsoring organizations, including the Canadian International 

Development Agency (CIDA) who funded 29 participants from developing countries, and 

additional funding <strong>to</strong> speakers from GOFC-GOLD (Global Observation of Forest and Land 

Cover Dynamics), Agriculture and Agri-Food Canada, and Environment Canada. 

Sherman Nelson of Agriculture and Agri-Food Canada (AAFC) made welcoming remarks on 

behalf of the Government of Canada. He stated that critical policy decisions need <strong>to</strong> be 

based on sound scientific information, and Earth Observation (EO) has a key role in 

providing this. He challenged participants <strong>to</strong> find ways <strong>to</strong> increase the number of operational 

applications that EO can provide sound scientific information for the policy community. 

Brenda Brindle of Alberta Agriculture and Rural Development welcomed participants on 

behalf of the Government of Alberta. The province exports $ 8 billion (Can.) of agricultural 

products annually, and the growing population is putting pressure on agriculture. She 

emphasized that coordinated and relevant knowledge is key <strong>to</strong> good management decisions, 

and so moni<strong>to</strong>ring systems are needed. 

David Grimes, GEO Principal for Canada and Assistant Deputy Minister at Environment 

Canada, gave a Canadian perspective of GEO and GEOSS (Global Earth Observation System 

of Systems). The vision of GEOSS is ‘To realize a future wherein decisions and actions for 

the benefit of humankind are informed by coordinated, comprehensive and sustained Earth 

observations and information’. He provided an overview of GEO and GEOSS and a 

description of some of the related work being done both in Canada and abroad. 

Anna Pacheco of AAFC presented the workshop overview, objectives and desired outcomes, 

opening with a quotation from Dr. Robert H. Goddard: “It is difficult <strong>to</strong> say what is 

impossible, for the dream of yesterday is the hope of <strong>to</strong>day and the reality of <strong>to</strong>morrow.” She 

observed that the Agriculture community has not yet had a strong and coordinated voice with 

respect <strong>to</strong> their EO requirements, and only through such coordination that the community can 

make the most effective use of existing resources, and influence the next generation of 

technologies. She proposed that we must strive <strong>to</strong> get the technology and the methods that 

are proven in<strong>to</strong> the hands of the broad agriculture community. This workshop with its 

various sessions will provide one step <strong>to</strong>wards these goals. 

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Jai Singh Parihar of Indian Space Applications Centre presented the GEO <strong>Agricultural</strong> 

Moni<strong>to</strong>ring Community of Practice. He outlined that the reasons <strong>to</strong> moni<strong>to</strong>r world 

agriculture are: 

• Early Warning of Harvest Shortfalls or Abundance 

• Famine Early Warning for food insecure countries 

• Help moni<strong>to</strong>r market supply and demand - National <strong>Agricultural</strong> Production 

Moni<strong>to</strong>ring (Area Planted, Crop Condition, Yield) 

• Moni<strong>to</strong>ring of <strong>Agricultural</strong> Land Use Change (Cropland expansion, intensification, 

abandonment, change in crop type and cropping practices). 

Dr. Parihar introduced Chris Justice from University of Maryland, Co-Chair of the Global 

<strong>Agricultural</strong> Moni<strong>to</strong>ring (AG-07-03) task, and Jinlong Fan from the GEO Secretariat who 

both made brief remarks. 

Dr. Parihar defined Communities of Practice (CoP) as “<strong>Group</strong>s of people who share a 

concern, a set of problems, or a passion about a <strong>to</strong>pic by interacting on an ongoing basis” 

[Cultivating Communities of Practice, Harvard Business School Press, 2002]. He also 

presented the GEO <strong>Agricultural</strong> Moni<strong>to</strong>ring Task (Ag 0703); the near term task initiatives 

are: 

• Initiative 1: A Multi-source Production, Acreage and Yield (PAY) database 

• Initiative 2: Joint Experiments on Crop Assessment and Moni<strong>to</strong>ring (JECAM) 

• Initiative 3: Coordinated Data Initiatives for Global <strong>Agricultural</strong> Moni<strong>to</strong>ring 

(CDIGAM) 

• Initiative 4: GLAMSS Thematic Workshop Series (GTWS) 

• Initiative 5: Agriculture and Climate Change (initiative in development). 

Dr. Parihar presented the growth stages of a CoP and showed where he thinks this CoP is in 

maturity, as indicated by the vertical line in Figure 1. 

Bradley Doorn of NASA presented ‘Developing Agriculture Moni<strong>to</strong>ring Requirements and 

Recognizing the Earth Observation Gaps’. He indicated that NASA is giving away high 

volumes of data <strong>to</strong> foster discovery that will ultimately lead <strong>to</strong> societal benefits. He proposed 

that requirements for the community should be defined by category, including: 

• frequency, 

• latency, 

• accessibility, 

• spatial completeness, 

• relevance, 

• confidence, and 

• longevity 

in how these effect agriculture management and policy decisions. We must first ask “Why 

are the decisions needed and how are decisions made now?” He identified requirement gaps 

in confidence, completeness, frequency and relevance, citing examples of the critical 

indica<strong>to</strong>rs of vegetation health, soil moisture and vegetation type. 

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Brian Brisco (Canada Centre for Remote Sensing, CCRS) presented the state of <strong>SAR</strong> for 

agricultural moni<strong>to</strong>ring. He emphasized that no spaceborne <strong>SAR</strong> yet has been designed for 

land applications, so often the configuration is sub-optimal for agricultural moni<strong>to</strong>ring. From 

a technical perspective, the backscatter from the soil, vegetation and interaction terms are 

integrated and this confuses the interpretation of the satellite measurements; these terms need 

<strong>to</strong> be separated by multiple observations and new technology. Dr. Brisco highlighted the 

benefits of multi-frequency, multi-temporal, multi-incidence and multi- polarization 

approaches. Adding optical EO data can increase the accuracy dramatically. He presented 

the barriers <strong>to</strong> greater operational adoption of <strong>SAR</strong> and the remaining research and 

development (R&D) needs. 

5

Loose network 

of people with 

similar issues 

and needs 

Potential 

Agree on a 

common 

Vision and 

Goals 


Figure 1 Growth Stages of a CoP - Where Are We? 

We Are Here 

Members 

come 

<strong>to</strong>gether and 

launch a 

community 

Coalescing 

Identify and 

prioritize 

activities 

Engage partner 

organizations 

It forms an 

identity, takes 

charge of its 

practice and 

grows 

Maturing 

Set standards, 

Implement 

system 

components 

6 

The community is 

established and 

acts as the steward 

of its domain 

Stewardship 

The Ag Mon 

System of 

Systems is 

established and 

is operational 

The community 

has outlived its 

usefulness and 

people move on 

? 

Legacy 

Let go, define 

a legacy, and 

keep in <strong>to</strong>uch


2.2 <strong>SAR</strong> Data Providers 

Yves Crevier of Canadian Space Agency (CSA) opened this session, which brought <strong>to</strong>gether 

space agencies and companies that provide <strong>SAR</strong> data <strong>to</strong> the public. He gave two quotations, 

<strong>to</strong> frame his presentation: “Technology and science are only as good as our ability <strong>to</strong> use the 

information they provide.”, and “The successes are dependent on our ability <strong>to</strong> relay the 

information the final mile <strong>to</strong> the people.” He identified the following opportunities for the 

<strong>Agricultural</strong> Moni<strong>to</strong>ring CoP as: 

• To identify and prioritize the needs of the agriculture moni<strong>to</strong>ring community 

• To communicate the information requirements and operational needs <strong>to</strong> facilitate 

coordinated access <strong>to</strong> EO data. 

Similarly, opportunities for the <strong>SAR</strong>-operating space agencies are: 

• To open a dialogue with international science communities 

• To understand the data needs and requirements regarding agriculture moni<strong>to</strong>ring that 

can be addressed by <strong>SAR</strong> data products. 

The following <strong>SAR</strong> data providers presented the details of their satellites, <strong>SAR</strong> sensors, 

agricultural applications, and R&D programs: 

• Comisión Nacional de Actividades Espaciales (CONAE, Argentina’s National 

Commission on Space Activities) 

• Canadian Space Agency (CSA) 

• Deutsches Zentrum für Luft- und Raumfahrt (DLR, German Aerospace Centre) 

• European Space Agency (ESA) 

• Japan Aerospace Exploration Agency (JAXA) 

• National Aeronautics and Space Administration (NASA) 

• MacDonald Dettwiler and Associates Ltd (MDA). 

Figure 2 shows typical roughness and vegetation cover from a sample field campaign in 

Argentina. 

Denis Auger of CSA predicted that 250 EO satellites will be launched over the next ten 

years. Malcolm Davidson of ESA described the future launch of the Sentinel-1 satellites, 

which will be user-driven, and will provide conflict-free operations. Routine Data Access 

will be available at no charge. 

During the question period, it was pointed out that in the next few years, there will be a 

number of <strong>SAR</strong> spaceborne sensors at L-band, C-band and X-band, so multi-frequency 

technology will be available <strong>to</strong> exploit by the science and user communities. There are still 

open science questions, including how best <strong>to</strong> use multiple frequencies, multiple incidence 

angles, polarimetry and multi-temporal acquisitions for each of the applications, such as crop 

yield and health, soil moisture and tillage. Providing scientific research <strong>to</strong> respond <strong>to</strong> these 

questions can help the space agencies <strong>to</strong> meet user needs. 

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High Roughness S1 

Low roughness S2 


Figure 2 Sample Field Campaign in Argentina 

Falda del Carmen Córdoba 

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Corn Stubble N4 

Wheat N5


2.3 <strong>Agricultural</strong> Productivity 

The ‘<strong>Agricultural</strong> Productivity’ technical session focused on international research and 

development for crop area moni<strong>to</strong>ring. Chakrapani Patnaik (Indian Space Research 

Organization, ISRO) described the rice crop moni<strong>to</strong>ring using <strong>SAR</strong> that has been operational 

in India since 1999. Jim Finnigan (Digital Environmental Management) described a 

demonstration of rice crop moni<strong>to</strong>ring in the Philippines. He showed that using coarser 

resolution data was much more affordable and impacted the accuracy very little. Two key 

advantages of remote sensing are that the results are unbiased and a remote sensing based 

survey is much faster (at least one order of magnitude faster) than a ground-based survey. 

Shao Yun (Institute of Remote Sensing Applications, China) presented the use of 

polarization signature analysis <strong>to</strong> discriminate rice crop areas, even in mountainous terrain. 

Jinsong Chen (Hong Kong Chinese University) highlighted his work on moni<strong>to</strong>ring rice and 

sugarcane using ENVISAT-A<strong>SAR</strong> data. Nathan Torbrick (Applied Geosolutions) outlined 

work on mapping rice agriculture and calculating Greenhouse Gas emissions in China and 

US. 

Jai Singh Parihar (ISRO) demonstrated how a wide range of optical and active / passive 

microwave sensors can provide a wealth of information, emphasizing the need for integration 

of all of these technologies. He suggested that we need <strong>to</strong> continue <strong>to</strong> explore other sensors 

which are suitable for other agricultural regions where other crops are planted. 

2.4 Soil and Water Availability 

In the ‘Soil and Water Availability’ session, scientists demonstrated advanced research in<strong>to</strong> 

techniques for separating the components of the radar backscatter signal arising from the soil 

surface, vegetation (volume scattering) and double bounce (from stalks of plants and trunks 

of trees), as shown in Figure 3. Soil roughness complicates this situation further. On bare 

soil, moisture can be accurately measured with <strong>SAR</strong> (within 5% accuracy). By using L-band 

(PAL<strong>SAR</strong>), Francesco Mattia (Istitu<strong>to</strong> di Studi sui Sistemi Intelligenti per l’Au<strong>to</strong>mazione, 

Italy) showed that multi-temporal soil moisture maps underlying cereal crops can be 

retrieved from Scan<strong>SAR</strong> images at HH polarization and a priori information on soil moisture 

content. 

Daniel Sabel (Institute of Pho<strong>to</strong>grammetry and Remote Sensing, Vienna) stated that the 

dynamics of soil moisture should be measured frequently, optimally on a daily basis, with 

spatial resolution of about 1 km. <strong>SAR</strong> systems have greater spatial resolution and less 

temporal resolution, while scatterometers have good temporal resolution but inadequate 

spatial resolution. Soil moisture remote sensing has recently matured from research <strong>to</strong> 

services; ASCAT soil moisture data are distributed in near-real-time via EUMETCast since 

December 2008. The scatterometer method has been successfully transferred <strong>to</strong> the <strong>SAR</strong> 

domain. The best <strong>SAR</strong> coverage is provided by ENVISAT A<strong>SAR</strong> Global Moni<strong>to</strong>ring mode, 

and this is used <strong>to</strong> produce Surface Soil Moisture at 1 km resolution. 

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Figure 3 Scattering Mechanisms 

Émilie Bériaux (Université catholique de Louvain, Belgium) hightlighted research indicating 

that the estimation of soil moisture from <strong>SAR</strong> data allows Leaf Area Index (LAI) estimation 

from <strong>SAR</strong> data without fields measurements, and the cross-polarizations have best 

performance in LAI estimation. Amine Merzouki (AAFC) assessed the performance of a 

number of models and decompositions for measuring soil moisture from <strong>SAR</strong>. François 

Charbonneau (CCRS) showed techniques for using polarimetry <strong>to</strong> separate the components 

of the backscatter signal and assimilating the soil moisture in<strong>to</strong> hydrogeological models. 

2.5 Land Cover and Land Management 

The ‘Land Cover and Land Management’ technical session provided advanced research on 

crop identification and crop condition. Henning Skriver (Technical University of Denmark, 

Denmark) presented research on the accuracy of crop classification from different <strong>SAR</strong> 

modes. He found that: 

• L-band and C-band results were comparable. 

• Multitemporal acquisitions are essential especially for non-polarimetric modes 

• Polarimetric data may only need a few acquisitions. 

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Anne Smith (AAFC) described the data collection for their research project “Investigating 

RADARSAT-2 as a Tool for Moni<strong>to</strong>ring Grassland in Western Canada”. She proposed that 

RADARSAT-2 has potential <strong>to</strong> contribute <strong>to</strong> two of the project’s three objectives, namely: 

• the spatial extent and fragmentation of native grasslands, 

• the invasive plant species, leafy spurge, on native grasslands. 

Carlos Cotlier (Universidad Nacional de Rosario, Argentina) highlighted recent work on 

identifying crops on small fields in the urban fringe. He concluded that Radarsat-2 Ultra 

Fine Beam images are optimum for land use detection of horticulture intensive farming, and 

they could detect small plots less than an acre in size. To discriminate the crops, he 

recommends using two consecutive Ultra Fine Beam images (24 days apart) with different 

polarizations, (e.g. one HH and the other HV), and creating a color composite image HH-HV 

or HH/HV. 

Christiane Schmullius (Friedrich-Schiller-University Jena, Germany) presented “Preparing 

for Sentinel-1: Operational Capabilities of C-Band Multi-Resolution and Multi-Polarized 

<strong>SAR</strong> Data for Crop Mapping”. She concluded that: 

• Multitemporal, multipolarized C-Band <strong>SAR</strong>-Data proves very high potential for crop 

mapping. 

• Optimal polarizations are HV and VV; HH has less information content. 

• Temporal resolution is more important for crop mapping than geometric resolution. 

She also demonstrated that the overall accuracy could be increased <strong>to</strong> 85% by combining the 

<strong>SAR</strong> results with Corine Land Cover 2000 using object segments and a majority function for 

allocation of pixels <strong>to</strong> segments. 

2.6 Capacity Building Activities and Opportunities 

This session described specific programs and projects related <strong>to</strong> capacity building and 

opportunities for developing countries <strong>to</strong> take advantage of <strong>SAR</strong> technology. Stéphane 

Chalifoux (CSA) presented the SOAR (Science and Operational Applications Research) 

Program. He challenged the <strong>Agricultural</strong> Community of Practice <strong>to</strong> discuss with CSA such 

opportunities as opening a new initiative, implementing a special project (perhaps through 

JECAM) or making a general request <strong>to</strong> all space agencies with <strong>SAR</strong> sensors. 

Ron Caves (MDA) described Agri<strong>SAR</strong> 2009, a campaign with the following objectives: 

• Simulate Sentinel-1 data products especially in terms of repeat observation, resolution 

and polarization. 

• Evaluate the performance of Sentinel-1 for agricultural and land cover products based 

on dense time series of radar data. 

• Evaluate the added value of full and compact polarimetry for the same information 

products. 

• Develop methodology for the generation and validation of agricultural and land cover 

map information products based on C-band radar data. 

Data was collected over three diverse agriculture test sites during 2009, and processing is ongoing. 

Figure 4 shows a RADARSAT-2 Fine Quad Mode of Flevoland in the Netherlands. 

11


Figure 4 RADARSAT-2 Fine Quad Mode Scene of Flevoland, Netherlands 

Tamuka Magadzire (FEWS-NET, Botswana) described FEWS-NET (Famine Early Warning 

Systems Network), which is a USAID-funded project dedicated <strong>to</strong> strengthening the abilities 

of countries/ governments and national/ regional organizations <strong>to</strong> manage threats of food 

insecurity and famine through generating and providing information about food security <strong>to</strong> a 

wide range of users. He presented both agricultural moni<strong>to</strong>ring and training/capacity 

building activities of FEWS-NET in Africa, central Asia and Central America/Caribbean. He 

emphasized a number of lessons learned from his training and capacity building experience. 

He highlighted approaches that DO NOT work include: 

• Providing training that is not based on real demand from clients. 

12


• Providing training without clients having a clear idea of how they want <strong>to</strong> use the 

<strong>to</strong>ols or software, or how it fits in<strong>to</strong> the big picture. 

• Providing training <strong>to</strong> the wrong personnel or those who are not going <strong>to</strong> do the work. 

• Training only one person in the institution, given the high turnover observed. 

• Keeping the training purely technical, and excluding high level management from the 

framework of the capacity building. 

• Providing training for <strong>to</strong>ols requiring resources that are not available. 

He continued by saying that approaches that DO work include: 

• Push versus Pull: The best training is provided on demand; this indicates a real 

interest from the institution or partners receiving the training. 

• Practical buy-in: when the institution puts some resources in<strong>to</strong> the training, it is more 

likely that there is a real interest in using the <strong>to</strong>ol. 

• Political will: high-level support within the institution is essential. 

• Context: The training needs <strong>to</strong> be embedded within a larger institutional and 

operational framework so that: 

o Trainees and management are made aware of the benefits of the 

enhancements. 

o New expectations are set at a managerial level based on newly (<strong>to</strong> be) 

acquired skills. 

o The training enhances existing operating procedures and plans. 

o There is greater focus on end-<strong>to</strong>-end application including processing, 

interpretation and analysis. 

• Relevance: the persons that receive the training should be those who are going <strong>to</strong> use 

the <strong>to</strong>ols or software inside the institutions. (Often, regional training programs do not 

result in take-up back home.) 

• Independent Training: Providing detailed comprehensive tu<strong>to</strong>rials and manuals that 

can be followed by trainees without the presence of the trainer. 

• Sustainability: institutionalize the capacity building and training programs, preferably 

through educational institutions. 

• Ensure continuity and practical utility of the training: 

o Ongoing communications between the trainer and the trainees that guarantee a 

cycle of information that is being worked with the <strong>to</strong>ols. 

o Follow up the questions or doubts of the trainees in order not <strong>to</strong> lose their 

interest in the <strong>to</strong>ols. 

Johnson Owaro (Office of the Prime Minister - Government of Uganda) presented 

“Developing EO Capacity Building Programmes for Agriculture Moni<strong>to</strong>ring”. His 

thoughtful and thought-provoking presentation linked GEO plans and aspirations with the 

real needs of developing countries. He showed that a Military Decision <strong>Support</strong> System has 

the following components: 

• Identify the problem 

• Understand the problem 

• Examine courses of action 

• Determine course of action 

13


• Assess capacity <strong>to</strong> deal with the problem 

• Fill existing capacity gaps (Train, mobilize resources) 

• Execute course of action. 

He suggested that this would a good approach <strong>to</strong> connect EO R&D (Scientific knowledge) 

with local decision making processes. 

14


3. Breakout Sessions 

3.1 Findings of Pre-workshop Survey 

Don Ball (a consultant and meeting facilita<strong>to</strong>r from DB Geoservices Inc, Canada) presented 

the findings from the pre-workshop survey. These findings appear in Annex 1. Roughly half 

of the respondents work at federal governments, and one third at universities. The <strong>to</strong>p twelve 

applications of <strong>Agricultural</strong> Moni<strong>to</strong>ring Data are shown in Figure 5. 

Figure 5 Top Twelve Applications of <strong>Agricultural</strong> Moni<strong>to</strong>ring Data 

50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 

Crop Identification 

Crop Area 

Land Cover 

Crop Yield 

Land Use 

Drought Moni<strong>to</strong>ring 

Soil Moisture 

Crop Health 

Flood Moni<strong>to</strong>ring 

Soil Type 

Irrigation 

Tillage Moni<strong>to</strong>ring 

Approximately 56% of moni<strong>to</strong>ring applications are in the research stage; the remainder is 

split evenly between pilot/demonstration and operations. In answer <strong>to</strong> the question: “How 

important is the use of <strong>SAR</strong> data for your agricultural moni<strong>to</strong>ring applications?” 45% replied 

‘Increasingly’ and 39% said ‘Critical’. The questions that were asked about the importance 

of fac<strong>to</strong>rs that could encourage greater use of <strong>SAR</strong> data drew responses that all of the options 

provided are important. There was relatively little difference in scores between the most 

important and the least important responses. 

15


Don Ball then gave instructions for the breakout sessions. The discussions of Scientific and 

Operational issues were combined in<strong>to</strong> one session of three hours, while the discussion of 

Capacity Building and Transition <strong>to</strong> Operations <strong>to</strong>ok place for ninety minutes on Wednesday 

afternoon. The breakout groups were asked <strong>to</strong> fill out Table 1. 

Table 1 Recommended Approach <strong>to</strong> Breakout Sessions 

Challenge Solution Action/ Next Steps Owner / Lead 

(What is discouraging 

greater use of <strong>SAR</strong> 

data) 

(High level or 

strategic step(s) <strong>to</strong> 

overcome the 

challenge) 

(The first few specific 

or tactical steps <strong>to</strong> 

implement the 

solution) 

16 

(Class of stakeholders 

who could undertake 

the action(s) e.g. 

governments, data 

providers, 

universities, …) 

In order <strong>to</strong> build a Recommended Approach, the groups were asked <strong>to</strong> identify the key 

challenges (fac<strong>to</strong>rs which discourage greater use of <strong>SAR</strong>) and for each challenge what are 

the main solutions (high level or strategic step(s) <strong>to</strong> overcome the challenge) and the main 

actions or next steps (the first few specific or tactical steps <strong>to</strong> implement the solution). The 

groups were also asked, time permitting, for each action <strong>to</strong> identify recommended owner or 

lead (class of stakeholders who could undertake the action(s) e.g. governments, data 

providers, universities, …). The owner / lead was specifically not supposed <strong>to</strong> be an 

individual nor a specific organization, but rather a class of stakeholders. 

The breakout groups were divided according <strong>to</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring applications: 

• Crop Production 

• Water Availability 

• Land Management. 

Workshop participants were given the choice of which application category they would like 

<strong>to</strong> join. The workshop organizers felt that the groups might consider different fac<strong>to</strong>rs, 

depending on which of these application categories they related most closely <strong>to</strong>. 

To facilitate the process of identifying key challenges/ solution/ actions, participants were 

provided with the ideas that were included in the survey. <strong>Group</strong>s were encouraged <strong>to</strong> build 

these ideas and provide innovative ideas not covered by the survey results.


3.2 Scientific and Operational Requirements, Challenges and 

Strategies 

This section summarizes the main findings of the three breakout groups for Scientific and 

Operational Requirements, Challenges and Strategies. Although the three groups were 

divided along application lines (Crop Production, Water Availability, Land Management), 

their conclusions were remarkably consistent. They further concluded that the differences 

between the challenges, solutions, actions and owners for Scientific use and Operational use 

were similar, and should be treated as a universal set of recommendations. The findings of 

the three breakout groups are shown in Appendix C. 

The highest priority challenge of the participants was <strong>SAR</strong> Data Access, which had several 

aspects: 

• Reduced data cost 

• Improved data policies (e.g. data access and sharing, data continuity) 

• Easier data discovery and order process (e.g. global portal) 

• Greater access <strong>to</strong> new acquisitions 

• Greater access <strong>to</strong> his<strong>to</strong>rical data archives 

• Coordination <strong>to</strong> existing in-situ data 

• Data sharing based on international standards (e.g. Open Geospatial Consortium or 

OGC) 

• Access <strong>to</strong> geometrically corrected and ortho-rectified or ready-<strong>to</strong>-use data. 

The second priority challenge was the Need <strong>to</strong> Reduce the Gap between Research and 

Operational Users. This was manifested in the following forms: 

• Integration of expertise and knowledge domain specific with Remote Sensing (e.g. 

soil + hydrology) 

• Community awareness of <strong>SAR</strong> data 

• Education and technology transfer 

• Development and improved availability of models or methods <strong>to</strong> derive information 

from data 

• Availability of operational software. 

The third priority challenge was related <strong>to</strong> Satellite Configuration, which had the following 

aspects: 

• New ‘moni<strong>to</strong>ring focussed’ <strong>SAR</strong> systems optimized for specific applications with 

improved design of future systems 

• Improved spatial (swath) coverage 

• Higher spatial (pixel) resolution 

• Desired <strong>SAR</strong> frequency (wavelength) available 

• Desired <strong>SAR</strong> polarization available 

• Improved revisit/re-look of satellite passes 

• Improved timeliness of satellite data access. 

17


The fourth priority challenge was Policy of Commitment <strong>to</strong> Data Continuity. 

The fifth priority challenge was Tool Development and Research, which had the following 

aspects: 

• Geospatial data assimilation and integration 

• Improved availability of models or methods <strong>to</strong> derive information from data 

• Improved data interoperability or integration 

• Global Representation (i.e. lack of research on arid regions) 

• Development of backscatter models. 

The results of these discussions are summarized in Table 2. As a first step on acting on these, 

Appendix D provides a list of resources from the Alaska Satellite Facility. 

18


Table 2 Recommended Approach for Scientific and Operational Challenges 

Challenge Solution(s) Action/ Next Steps Owner/ Lead 

<strong>SAR</strong> Data Access • Subsidize data <strong>to</strong> enable free / 

reduced cost data distribution. 

• Data Sharing policy. 

• Global database with in-situ data. 

• Awareness of existing catalogs. 

• Relaxing data policies on 

archived data (EO data has shelf 

life, after which data should 

simply be made freely available). 

• Process his<strong>to</strong>rical data <strong>to</strong> make it 

available <strong>to</strong> users. 

• Coordinate data procurement <strong>to</strong> take 

advantage of economies of scale. 

• Encourage international space agencies 

<strong>to</strong> consider data exchanges <strong>to</strong> meet the 

needs of the agriculture community. 

• Identify / secure funding <strong>to</strong> pay for data 

/ services. 

• Look at existing user payment models 

and other experience of successful 

communities (i.e. weather community). 

• User community communicates / 

lobbies data needs <strong>to</strong> space agencies. 

• Develop a consensus on data policy 

needs for agriculture and design a 

strategy <strong>to</strong> negotiate liberal data 

policies. 

• List of 10 URLs of key portals on the 

GEO web site. 

19 

• GEO 

• Space Agencies 

• Operational centres 

• User Gov’t agencies 

• <strong>Agricultural</strong> CoP 

• Regional Organizations 

(Agriculture Departments)



Need <strong>to</strong> Reduce the 

Gap between 

Research and 

Operational Users 

• Closer interactions between 

research / operational 

communities. 

• Financial support for converting 

research in<strong>to</strong> operations. 

• Outreach <strong>to</strong> decision-makers. 

• Access <strong>to</strong> and development of 

training courses; online courses / 

materials; combination of 

regional / local workshops; 

‘training of trainers’. 

• Capacity building in developing 

countries (i.e. through JECAM). 

• Lobby governments <strong>to</strong> develop policies 

<strong>to</strong> transition research in<strong>to</strong> operations. 

• GEO should disseminate training 

opportunities. 

• Greater access <strong>to</strong> software. 

• Applications Outreach Program. 

• Establish a website portal that includes 

training on <strong>SAR</strong> methods and best 

practices for agriculture applications, 

including manuals, glossary of terms, 

list of experts, user forums and links <strong>to</strong> 

<strong>SAR</strong> data providers and other resources. 

• Through the GEO web site, provide 

links <strong>to</strong> national and international 

announcements of opportunities and 

other funding opportunities. 

• Enhance scientific exchanges through 

annual/bi-annual agriculture workshops. 

• Develop an inven<strong>to</strong>ry of international 

science activities and <strong>SAR</strong> experts. 

• Target special journal and book 

publications <strong>to</strong> disseminate state of the 

art science, specific for agriculture. 

• Document best practices or approaches 

<strong>to</strong> designing area sample frameworks, 

data pre-processing and processing, 

methods and accuracy assessments 

related <strong>to</strong> <strong>SAR</strong> and agriculture. 

• Establish international super sites <strong>to</strong> 

facilitate research collaborations. 

20 

• Donor agencies’ 

governments 

• GEO 

• CEOS 

• Universities 

• UN 

• Space Agencies 

• Research Institutions 

• JECAM



Satellite 

Configuration 

Policy of 

Commitment <strong>to</strong> Data 

Continuity 

Tool Development 

and Research 

• Define and Document the 

requirements of the agriculture 

community with respect <strong>to</strong>: 

• Spatial resolution 

• Spatial coverage 

• Temporal revisit/re-look 

• Data latency/delivery 

• <strong>SAR</strong> sensor parameters 

(frequency, polarizations, 

angles). 

• Government & Commercial 

engagement, considering 

developed and developing 

countries. 

• Represent all the <strong>SAR</strong> 

Capabilities in the policy (i.e. L- 

Band). 

• Improved availability of models 

or methods <strong>to</strong> derive information 

from data (Best Practices re: 

potential applications of <strong>SAR</strong>). 

• Development of widely available 

algorithms (e.g. in commercial 

packages). 

• Provide access <strong>to</strong> <strong>to</strong>olbox 

<strong>to</strong>gether with the data. 

• Stimulate the development of the 

<strong>to</strong>ols and its marketing. 

• Promote educational software. 

• End user assessment oriented <strong>to</strong> 

regional requirements. 

• Constellation approach with operational 

satellites – multi-frequency / multi-res. 

• Launch of more satellites. 

• Lobby for adequate mission design. 

• Define globally-distributed ‘supersites’. 

• Experimentation with compact 

polarimetry technology. 

• Different satellites operating on fixedschedule 

tasking and flexible-tasking 

operational models. 

• End user commitment 

• Demonstrations <strong>to</strong> End Users 

• More research in developing new, valid 

algorithms. 

• Create an international team <strong>to</strong> assess 

interoperability among sensors and <strong>to</strong> 

design approaches <strong>to</strong> inter-calibrate 

among sensors. 

• Inven<strong>to</strong>ry of training and existing <strong>to</strong>ols. 

• Regional approach, Global initiative 

(i.e. multilingual). 

21 

• International Water 

Research Institute 

• CGIAR (Consultative 

<strong>Group</strong> on International 

<strong>Agricultural</strong> Research) 

• Space Agencies/ 

Governments 

• Soil Community 

• Regional Communities 

• CEOS 

• JECAM 

• GEO 

• Ag CoP 

• Universities 

• GEO 

• Space Agencies


3.3 Transitioning from Research <strong>to</strong> Operational Use and Capacity 

Building 

This section summarizes the main findings of the three breakout groups for Transitioning 

from Research <strong>to</strong> Operational Use and Capacity Building. Again, the findings of the three 

breakout groups are shown in Appendix C. 

The first priority challenge was <strong>SAR</strong> Data, in particular the following attributes: 

• Cost 

• Mode of purchase 

• In-situ data 

• Interoperability. 

The second priority challenge was Training and Acquiring Knowledge <strong>to</strong> Use the Data 

and Understanding the Application of the Data. This challenge had the following aspects: 

• Cost of training 

• Location 

• Language 

• Relevancy. 

The third priority challenge was Tools, including: 

• Software <strong>to</strong>ols for different levels of users 

• Ease of use of Software <strong>to</strong>ols 

• Hardware 

• Complementary (field) <strong>to</strong>ols 

• Accessing technical support. 

The fourth priority challenge was Infrastructure. Considerations here consisted of: 

• Internet capacity 

• Human capacities 

• Institutional capacity. 

The fifth priority challenge was Managerial and Political <strong>Support</strong>. 

The main Challenges/ Solutions/ Actions/ Owners are shown in Table 3. 

22


Table 3 Recommended Approach for Transitioning <strong>to</strong> Operations and Capacity Building 


<strong>SAR</strong> Data • Regional Nodes of receiving 

stations. 

• Data buy. 

• Network of in-situ data. 

• Data standards pro<strong>to</strong>cols. 

• Establishing programs that could 

provide data <strong>to</strong> developing 

countries. 

• Augment data for use with free 

software. 

• Inven<strong>to</strong>ry of various issues. 

• Regional web sites. 

• Replicate existing programs (GMES) <strong>to</strong> 

supply data <strong>to</strong> developing countries. 

23 

• GEO with Space Agencies 

• Agriculture Community of 

Practice. 

• Development Agencies.



Training and 

Acquiring 

Knowledge <strong>to</strong> Use the 

Data and 

Understanding the 

Application of the 

Data 

• Demo of utility of data 

application. 

• Online training. 

• Multi-lingual regionally 

appropriate training. 

• Thematically relevant training. 

• Use programs similar <strong>to</strong> Canadian 

EO Application Development 

Program. 

• Enhance cooperation among the 

data providers, researchers and 

user community. 

• Invest in universities <strong>to</strong> provide 

sustainable training. 

• Assessment of training needs. 

• Inven<strong>to</strong>ry of training resources and 

gaps. 

• Development of regional strategies. 

• Keep programs such as SOAR open on 

an on-going basis, for example 

Terra<strong>SAR</strong>. 

• International Symposium on Remote 

Sensing in Agriculture. 

• Identify available funding projects 

through international development 

agencies. 

• Develop and implement public outreach 

programs in schools and universities. 

• Initiate ‘<strong>SAR</strong> Day’ as an educational 

outreach opportunity. 

• Multi-media outreach products and 

video clips. 

• Network of networks. 

• Technical & educational exchange 

programs on GEO portal. 

• Annual forums. 

• ‘Train the trainer’ program – use the 

International Space University (ISU) 

infrastructure. 

24 

• GEO. 

• Universities and research 

institutions. 

• Space Agencies. 

• Development Agencies. 

• Universities. 

• User communities.



Tools • Grants for purchasing hardware 

and software and field <strong>to</strong>ols. 

• Open Source software. 

• Scientific aid. 

• Availability of free software 

solutions. 

• Reduced price for universities. 

• Develop enhanced <strong>to</strong>ols for data 

users. 

Infrastructure • International support for Internet 

capacities. 

• Centres of excellence. 

Managerial and 

Political <strong>Support</strong> 

• Training of trainers. 

• Develop cost-benefit product <strong>to</strong> 

inform decision makers. 

• Present the information products. 

• Communication of the societal 

benefits from the scientists/ 

researchers <strong>to</strong> the decision 

makers, funding agencies and 

industries (insurance agencies). 

• Demonstration of benefits <strong>to</strong> 

policy makers. 

• Assessment of user needs. 

• Inven<strong>to</strong>ry of free software. 

• Lobby Aid agencies. 

• Provide reduced cost for software with 

training programs. 

• Development agencies sponsor licenses 

for software. 

25 

• Community of Practice 

with end users. 

• GEO. 

• Software developers. 

• Space Agencies. 

• Development Agencies. 

• Analysis of Capacity Gaps. • Community of Practice 

with GEO-Capacity. 

• Derive demonstration products <strong>to</strong> prove 

benefits. 

• Share demo products (success s<strong>to</strong>ries) 

via ‘Geo Portal’. 

• Researchers. 

• Developers. 

• End-users. 

• GEO.


4. Panel Discussion 

A concluding panel discussion was convened where several experts representing different 

user communities were asked <strong>to</strong> comment on the recommendations. The participants were: 

• Space Agencies: Gladys Rodriguez Ortega and Marc Thibeault (CONAE) 

• Value Added Community: Trevor Taylor (PCI Geomatics) 

• Operational <strong>Agricultural</strong> Moni<strong>to</strong>ring Community: Ian Jarvis (AAFC) 

• University Researchers: Christiane Schmullius (Friedrich-Schiller-University Jena, 

Germany) 

• Developing Country Scientists / Users: El Sayed Hermas (National Authority for 

Remote Sensing and Space Sciences, Egypt). 

4.1 Space Agency Summary 

On behalf of the space agency community, Gladys Rodriguez Ortega and Marc Thibeault 

provided the following response <strong>to</strong> the recommendations. 

1. CONAE prioritizes the use of the data, especially for users in Argentina. 

2. CONAE promotes educational development, including outreach <strong>to</strong> children at 

a young age 

3. The EO community needs better coordination between the Space Agencies, so 

that each country can use the data of other countries. 

4. Space Agencies also need better coordination with the <strong>Agricultural</strong> CoP, 

suggesting that GEO and CEOS are important and can help in this 

coordination. 

5. There is a strong need for a coordinated multi-frequency <strong>SAR</strong> constellation. 

6. More funding is needed for R&D and pilot programs. 

4.2 Value Added Community Summary 

On behalf of the value-added community (software and <strong>to</strong>ol providers), Trevor Taylor 

empasized the following points: 

1. PCI is a member of the Strategic Council of the Open Geospatial Consortium 

(OGC). The OGC may be relevant <strong>to</strong> the <strong>Agricultural</strong> CoP. 

2. We need more accessible, low cost data, which would have the benefits of: 

a. Reducing the gap between research and operations. 

b. Deriving information products like flood maps. 

c. Facilitating the flow of information <strong>to</strong> consumers. 

4.3 Operational <strong>Agricultural</strong> Moni<strong>to</strong>ring Community Summary 

26


On behalf of the operational agricultural moni<strong>to</strong>ring community, Ian Jarvis made the 

following points: 

1. This meeting has identified many issues and actions, such as satellite 

configuration, gaps in coverage, etc. 

2. The greatest challenge is institutional. In Canada, it is difficult <strong>to</strong> get 

sufficient support for operations. Many actions are reactive, i.e. a response <strong>to</strong> 

disasters. This <strong>Agricultural</strong> CoP can transform the paradigm <strong>to</strong> be proactive, 

such as creating a network of research sites <strong>to</strong> work with users and policy 

experts <strong>to</strong> understand user needs. 

4.4 University Researcher Community Summary 

On behalf of the academic research community, Christiane Schmullius emphasized the 

following points: 

1. It has been 15 years since the SIR-C/X missions. The algorithms <strong>to</strong> extract 

information exist but are not readily accessible for operational users. We are 

now in a golden age of <strong>SAR</strong> since the launch of Envisat in 2002 and ALOS in 

2006. After 1994, a lot of knowledge was lost, especially in the US. We are 

re-inventing the wheel. The knowledge is there; why is it not used? 

2. Data is not the issue; we need a body <strong>to</strong> produce interpreted products. A lot of 

work remains <strong>to</strong> operationalize the existing knowledge. 

During the Q&A period afterward, Brian Brisco added that the basics are known, and the 

challenge is how <strong>to</strong> apply them in each domain. 

4.5 Developing Country Scientists / Users Summary 

On behalf of the developing county scientists and users, El Sayed Hermas outlined that the 

existence of data is not the problem, but that we need a strategy for dissemination of data. 

The main issues with respect <strong>to</strong> <strong>SAR</strong> usage in developing countries are: 

1. Productivity of land and water. 

2. Building capacity through infrastructure and training. 

He further emphasized, as an example that the Middle East use optical data but not <strong>SAR</strong>, 

when <strong>SAR</strong> would provide an ideal technology for this environment. 

4.6 Overall Workshop Summary 

The workshop was concluded by Chris Justice from the GEO AgCOP. He emphasized that 

based on the discussions, <strong>SAR</strong> data are ready for prime-time. The niche where <strong>SAR</strong> is 

strongest is spatially: 5 – 20 m resolution, and temporally: 1 or 2 images every 10 days. He 

presented Figure 6, a Conceptual Diagram of the <strong>Agricultural</strong> Moni<strong>to</strong>ring System of 

Systems. He challenged the Technical Committee <strong>to</strong> refine the diagram <strong>to</strong> include <strong>SAR</strong>. 

27


He continued by saying that current <strong>SAR</strong> data have not reached their full potential in terms of 

use and uptake; a number of obstacles have been identified, including costs, data complexity 

and software. 

There is a need <strong>to</strong> improve the transition from Research <strong>to</strong> Operations. Some methods are 

ready for operational use, but documented Best Practices are needed. The gap between 

Research and Operations gap was addressed in the first breakout session (see Table 2). He 

presented a conceptual model for transitioning Research <strong>to</strong> Operations (Figure 7). 

Operational users have specific needs, including reliable data flows, data continuity, and 

operational satellite systems. We are well positioned in terms of future <strong>SAR</strong> systems that can 

provide data continuity at present, but affordable, ready-<strong>to</strong>-use (orthorectified etc) data is 

needed. The same applies for <strong>to</strong>ols. Chris wondered if users could be charged according <strong>to</strong> 

the Gross Domestic Product of each country. This could make the data and <strong>to</strong>ols more 

affordable for developing countries who need the data as much as developed countries. 

Timely data is needed with improved temporal frequency (shorter revisit times). 

Constellations of satellites will address the temporal resolution issue, and several 

constellations are planned. The devil is in the detail; interoperable systems and acquisition 

strategies must be developed and put in<strong>to</strong> operation. 

Institutional obstacles need <strong>to</strong> be removed. Dr. Justice noted that there is no ‘L’ (for Land) in 

NOAA (National Oceanic and Atmospheric Administration). The US Geological Survey 

does not have the budget <strong>to</strong> launch operational satellites, nor does the US Department of 

Agriculture. 

There is the need for extensive capacity building on the use of <strong>SAR</strong> data; an ongoing training 

program is needed (see Table 3 with the results of the last breakout session). 

Research is identifying interesting new opportunities and applications for more multifrequency, 

multi-resolution, multi-polarization, multi-temporal, multi-incidence angle 

experiments, and JECAM has an important role <strong>to</strong> play. Proposals and acquisition requests 

are needed. The GEO Forest Carbon Tracking Task has a series of National Demonstra<strong>to</strong>r 

countries, and maybe the <strong>Agricultural</strong> Community could define super sites <strong>to</strong> achieve the 

same thing. 

The Landsat experiment has shown that making data free increases the data use enormously. 

He suggested that older archive <strong>SAR</strong> data should be made free <strong>to</strong> stimulate R&D and 

develop the market, then data providers could charge for newer data. 

28


Figure 6 Conceptual Diagram of the <strong>Agricultural</strong> Moni<strong>to</strong>ring System of Systems 

29

Experimental 

RS Systems 

e.g. MODIS, 

LDCM, EO1, JERS 


Operational 

RS Systems 

e.g. Sentinels, AVHRR, 

NPOESS VIIRS, 

Figure 7 Transitioning Research <strong>to</strong> Operations 

Fundamental 

and 

Global Change Science 

User Driven 

Research and Development 

Operational Pro<strong>to</strong>typing 

30 

Peer Review 

Publications 

Operational Use of the 

Data and Information 

Products 

Resource Management and Policy


Dr. Justice emphasized that we need Global Crop Calendars <strong>to</strong> answer the question: “Where 

are the data needed and when?” This information would lead <strong>to</strong> a global data acquisition 

strategy. Then the <strong>Agricultural</strong> Moni<strong>to</strong>ring CoP could have a focused planning meeting with 

the <strong>SAR</strong> Space Agencies <strong>to</strong> create a coordinated systematic acquisition strategy. A CEOS 

<strong>SAR</strong> focus group is needed similar <strong>to</strong> other CEOS working groups. 

The CEOS Land Surface Imaging (LSI) task group should address <strong>SAR</strong> Constellations. The 

timing needs for <strong>Agricultural</strong> Moni<strong>to</strong>ring should influence acquisition planning for Sentinel 1 

and 2 and other systems in planning. 

Dr. Justice pointed out that large workshops are expensive, but Latin America and Africa 

need attention, and general meetings on <strong>Agricultural</strong> Moni<strong>to</strong>ring should be considered for 

2010/2011. He suggested that the CoP should now move <strong>to</strong> more focused thematic smaller 

workshops (as discussed in GTWS) on aspects of <strong>SAR</strong> in the context of JECAM. Proposals 

for hosting workshops are welcome. 

There is a need for ongoing community interactions through the CoP, in order <strong>to</strong> make a 

stronger community voice in GEO. The CoP should implement recommendations arrived at 

by consensus, and they should lobby decision-makers. There is a need <strong>to</strong> fully integrate <strong>SAR</strong> 

practitioners in<strong>to</strong> the GEO Ag 0703 Task, but he would not recommend a <strong>SAR</strong> CoP. 

31


5. Recommendations 

Over the course of the workshop, many issues and recommendations were highlighted, but 

several key ones were given the highest priority for action by the partipcants. The 

recommendations that recurred over and over during the breakout groups and the panel 

discussion were the following: 

1. Organize a <strong>SAR</strong> component of the GEO Global <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Community of Practice (CoP) and its partners <strong>to</strong> coordinate initiatives related 

<strong>to</strong> the development of the <strong>Agricultural</strong> Moni<strong>to</strong>ring System of Systems. 

2. Pressure space agencies and data providers for lower cost <strong>SAR</strong> data and <strong>to</strong> 

remove restrictions on sharing and copying of data. 

3. Use JECAM (GEO Joint Experiment for Crop Area Moni<strong>to</strong>ring: 

Towards an <strong>Agricultural</strong> Moni<strong>to</strong>ring System of Systems) as a mechanism 

for capacity building in developing countries. 

4. Establish a website portal that includes training on <strong>SAR</strong> methods and best 

practices for agriculture applications, including manuals, glossary of terms, 

list of experts, user forums and links <strong>to</strong> <strong>SAR</strong> data providers and other 

resources. 

5. Encourage scientific exchanges, capacity building and training through 

GEO organized annual/bi-annual workshops. 

6. Establish international supersites <strong>to</strong> facilitate research collaborations. 

7. Document and clearly communicate data requirements of the agriculture 

community with respect <strong>to</strong>: spatial resolution; spatial coverage; temporal 

revisit/relook; data latency/delivery; and, <strong>SAR</strong> sensor parameters (frequency, 

polarizations, angles). These requirements should be communicated <strong>to</strong> the 

Space Agencies. 

8. Advocate the importance of data continuity policies through future satellite 

missions. 

9. Prioritize and coordinate development of training programs and 

analysis/application <strong>to</strong>ols and make these available <strong>to</strong> developing countries. 

32


6. Actions & Next Steps 

The following suggestions for actions and next steps were emphasized by the GEO AgCoP. 

The Workshop Technical Committee (with Co-Chairs Heather McNairn and Jai Singh 

Parihar) should generate the Meeting Report, including an Executive Summary and 

Recommendations. The presentations from this workshop should be placed on-line. 

The Canadian Journal of Remote Sensing will publish a Special Issue on <strong>Agricultural</strong> 

Moni<strong>to</strong>ring using <strong>SAR</strong>. Papers are solicited from applications of <strong>SAR</strong> and <strong>SAR</strong>/optical 

integration on the following <strong>to</strong>pics: 

• land cover and land use mapping and moni<strong>to</strong>ring 

• crop identification and area estimation 

• crop condition, productivity, and yield estimation 

• estimation of crop biophysical properties 

• land management mapping and moni<strong>to</strong>ring 

• mapping of soil properties 

• soil moisture estimation. 

The submission deadline is 31 January 2010. For submission instructions, see 

http://pubservices.nrc-cnrc.ca/clients/pdfs/cjrs_instruct_e.pdf 

The GEO Secretariat (with Jinlong Fan as the Agriculture Societal Benefit Area (SBA) point 

of contact) should raise the visibility of food security and Agriculture as a critical SBA and 

help <strong>to</strong> identify funds for capacity building. This should be a priority for GEO. This will be 

discussed at the <strong>Agricultural</strong> Outreach Focus at the GEO Ministerial meeting in Beijing in 

2010. 

The GEO Ag 0703 Task (with Jai Singh Parihar as Executive Direc<strong>to</strong>r) should: 

• Explore on line <strong>SAR</strong> tu<strong>to</strong>rials 

• Populate the CoP Web Site with <strong>SAR</strong> links, Tools and Methods 

• Complete the Best Practices Document. 

The Canadian GEO Secretariat and AAFC should pursue the JECAM Coordination role and 

also pursue on-line tu<strong>to</strong>rials. A Special Issue on Education and Training in Remote Sensing 

is in preparation by Brigitte Leblon at the Unversity of New Brunswick, and she invited 

people <strong>to</strong> send her titles of papers for this issue. 

33


Appendix A – Acknowledgements, Sponsors and Partners 

Acknowledgements 

The Workshop Organizing Committee wishes <strong>to</strong> acknowledge the positive support and 

encouragement received from the Government of Canada departments and agencies which 

support the activities and programs of the Canadian <strong>Group</strong> on Earth Observations (CGEO). 

The success of the workshop, training session, field trips, and side meetings would not have 

been possible without the considerable input and support of the many sponsors and partners 

listed below – our sincere thanks <strong>to</strong> all. The organizers also acknowledge the role of Don 

Ball, DB Geoservices Inc., in preparing draft versions of this Workshop Report and the 

Report on Results of the Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring. Special thanks 

<strong>to</strong> the Canadian International Development Agency (CIDA) <strong>to</strong> enable the travel of many 

sponsored participants. 

Most importantly, we thank all of the participants, many of whom travelled under very short 

notice from around the globe, for their significant input and contributions. If the success of 

an event can be measured by the number of persons still mingling in the meeting room after 

the late afternoon closing of the Workshop and a long week of activities – then it was indeed 

a successful event! 

Sponsors and Partners 

Government of Canada Sponsors – Canadian <strong>Group</strong> on Earth Observations 

Agriculture and Agri-Food Canada 

Canadian Space Agency 

Environment Canada 

Fisheries and Oceans Canada 

Foreign Affairs and International Trade Canada 

Health Canada 

Natural Resources Canada 

Parks Canada 

Statistics Canada 

International Partners 

GEO Global <strong>Agricultural</strong> Moni<strong>to</strong>ring Community of Practice 

GOFC-GOLD (Global Observation of Forest and Land Cover Dynamics) 

<strong>Group</strong> on Earth Observations (GEO Secretariat – Geneva, Switzerland) 

34


Travel Assistance for Sponsored Participants 

Canadian International Development Agency (CIDA) 


Environment Canada 

GOFC-GOLD (Global Observation of Forest and Land Cover Dynamics) 

Development and Delivery of the <strong>SAR</strong> Training Course 


Canada Centre for Remote Sensing, Natural Resources Canada 

Indian Space Research Organization 

Intermap Technologies 

MDA Corporation 

PCI Geomatics 

University of Calgary 

University of New Brunswick 

Corporate Sponsors 

MDA Corporation 

PCI Geomatics 

35


Appendix B – Agenda 

Workshop Day 1: Monday – 2 November (8:30-16:50) 

Session 1: Plenary and Keynote Addresses 

Session Chair: Ken Korporal (Canadian GEO Secretariat, Canada) 

8:30-8:45: Welcome and Logistics 

Ken Korporal (Canadian GEO Secretariat, Canada) 

8:45-9:15: Welcoming Remarks 

• Government of Canada: Sherman Nelson (Agriculture and Agri- 

Food Canada, Canada) 

• Province of Alberta: Brenda Brindle (Alberta Agriculture and 

Rural Development, Canada) 

9:15-9:45: GEO and GEOSS – A Canadian Perspective 

David Grimes (Environment Canada, Canada) 

9:45-10:00: Workshop overview, objectives, targeted outcomes 

Anna Pacheco (Agriculture and Agri-Food Canada, Canada) 

Jai Parihar (Indian Space Applications Centre, India) 

10:00-10:15: Health Break 

10:15-10:45: The GEO <strong>Agricultural</strong> Moni<strong>to</strong>ring Community of Practice 

Jai Parihar (Indian Space Applications Centre, India) 

10:45-11:15: Developing Agriculture Moni<strong>to</strong>ring Requirements and 

Recognizing the Earth Observation Gaps 

Brad Doorn (National Aeronautics and Space Administration, USA) 

11:15-11:45: <strong>SAR</strong> for <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Brian Brisco (Natural Resources Canada, Canada) 

11:45-13:00: Lunch 

Session 2: <strong>SAR</strong> Data Providers: Overview of satellite sensors, access <strong>to</strong> <strong>SAR</strong> 

data, on-going moni<strong>to</strong>ring initiatives and future <strong>SAR</strong> systems 

Session Chair: Yves Crevier (Canadian Space Agency, Canada) 

13:00-13:05: Introduction and Objectives 

Yves Crevier (Canadian Space Agency, Canada) 

36


13:05-13:30: Comision Nacional de Actividades Espaciales (CONAE) 

Marc Thibeault and Gladys Ortega (Argentina) 

3:30-13:55: Canadian Space Agency 

Denis Auger (Canada) 

13:55-14:20: German Aerospace Centre (DLR) 

Achim Roth (Germany) 

14:20-14:45: European Space Agency (ESA) 

Malcolm Davidson 

14:45-15:10: Japan Aerospace Exploration Agency (JAXA) 

Masa<strong>to</strong> Ohki (Japan) 

15:10-15:35: MacDonald Dettwiler and Associates Ltd. (MDA) 

Gordon Staples (Canada) 


Session 3: <strong>SAR</strong> <strong>Support</strong>ing <strong>Agricultural</strong> Mapping and Moni<strong>to</strong>ring: Case 

Studies 

Session 3a: <strong>Agricultural</strong> Productivity 

Session Chairs: Yolanda Fernandez-Ordonez (Colegio de 

Postgraduados, Mexico) and Jiali Shang (Agriculture and Agri-Food 

Canada, Canada) 

15:50-16:10: Operational Rice Crop Moni<strong>to</strong>ring Using <strong>SAR</strong> 

Chakrapani Patnaik (Space Applications Centre, ISRO, India) 

16:10-16:30: Building an Operational Rice Mapping Program Using <strong>SAR</strong>: An 

Example from the Philippines 

Jim Finnigan (Digital Environmental Management, Canada) 

16:30-16:50: Polarization Signature Analysis of Paddy Rice in Southern China 

Shao Yun (Chinese Academy of Sciences, Institute of Remote Sensing 

Applications, China) 

Evening Reception and Ice Breaker (17:30 – 19:00) 

Official Opening of Demo Area and Posters (18:30) 

37


Workshop Day 2: Tuesday – 3 November (8:30-17:10) 

Session 3: <strong>SAR</strong> <strong>Support</strong>ing <strong>Agricultural</strong> Mapping and Moni<strong>to</strong>ring: Case 

Studies 

Session 3a: <strong>Agricultural</strong> Productivity continued 

Session Chairs: Brigitte Leblon (University of New Brunswick, 

Canada) and Carlos Cotlier (Universidad Nacional de Rosario, 

Argentina) 

8:30-8:55: Moni<strong>to</strong>ring Rice and Sugarcane Using ENVISAT-A<strong>SAR</strong> Data 

Jinsong Chen (Institute of Space and Earth Information Science, Hong 

Kong Chinese University, Hong Kong) 

8:55-9:20: Mapping and Moni<strong>to</strong>ring of Rice with Multi-temporal PAL<strong>SAR</strong>: 

Developing Spatially and Temporally Explicit Products for Modeling 

Methane Emissions at Regional <strong>to</strong> National Scales 

Nathan Torbick (Applied Geosolutions, USA) 

9:20-9:50: Beyond C-band <strong>SAR</strong> for <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Jai Parihar (Space Applications Centre, ISRO, India) 


Session 3b: Soil and Water Availability 

Session Chairs: Brian Brisco (Natural Resources Canada, Canada) and 

Genya Sai<strong>to</strong> (Tohoku University, Japan) 

10:20-10:40: Use of Multi-Temporal <strong>SAR</strong> Data for Moni<strong>to</strong>ring Soil and Vegetation 

Parameters over <strong>Agricultural</strong> Surfaces 

Francesco Mattia (CNR-ISSIA, Bari, Italy) 

10:40-11:00: Soil Moisture Moni<strong>to</strong>ring with C-band <strong>SAR</strong> Sensors: Learning 

from Operational Scatterometer Services 

Daniel Sabel (Institute of Pho<strong>to</strong>grammetry and Remote Sensing, 

Vienna University of Technology, Austria) 

11:00-11:20: <strong>Support</strong>ing <strong>Agricultural</strong> Risk Management: An Active Microwave 

Approach <strong>to</strong> Identify Soil Moisture Extremes 

Amine Merzouki (Agriculture and Agri-Food Canada, Canada) 

11:20-11:40: Estimating <strong>to</strong>p soil moisture and analysis of 4 polarizations in C-band 

<strong>SAR</strong> imagery for wide scale maize LAI retrieval from <strong>SAR</strong> data 

Emilie Bériaux (Université catholique de Louvain, Faculty of 

Bioengineering, Agronomy and Environment, Belgium) 

38


11:40-12:00: Using Fully Polarimetric and Dual-Pol <strong>SAR</strong> for Soil Moisture 

Estimation under Vegetated Condition 

François Charbonneau (Natural Resources Canada, Canada Centre for 

Remote Sensing, Canada) 

12:00-13:30: Lunch Break 

Session 3c: Land Cover and Land Management 

Session Chairs: Anna Pacheco (Agriculture and Agri-Food Canada, 

Canada) and Roshanak Darvishzadeh (Shahid Beheshti University, 

Iran) 

13:30-13:50: Accuracy of Crop Classification from Different <strong>SAR</strong> Modes 

Henning Skriver (Technical University of Denmark, Denmark) 

13:50-14:10: Investigating RADARSAT-2 as a Tool for Moni<strong>to</strong>ring Grassland in 

Western Canada 

Anne Smith (Agriculture and Agri-Food Canada, Canada) 

14:10-14:30: The Use of RADARSAT-2 Ultra Fine Images in Horticulture Intensive 

Farming: Detecting Land Use and Crop Discrimination 

Carlos Cotlier (Universidad Nacional de Rosario, Argentina) 

14:30-14:50: Preparing SENTINEL-1: Operational Capabilities of C- Band Multi- 

Resolution and Multi-Polarized <strong>SAR</strong> Data for Crop Mapping. Results 

from the DLR ENVILAND and ESA Radar Cover Projects 

Christiane Schmullius (Institute of Geography, University Jena, 

Germany) 


Breakout 1: Bridging the Discovery <strong>to</strong> Delivery Continuum – Technical and 

Operational Requirements, Challenges and Strategies in 

Integrating <strong>SAR</strong> in<strong>to</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Session Chair: Don Ball (DB Geoservices Inc, Canada) 

15:10-15:40: Presentation of results from pre-workshop survey 

Don Ball (DB Geoservices Inc, Canada) 

15:40-17:10: Breakout on the technical and operational requirements, challenges and 

strategies for exploitation of <strong>SAR</strong> in agriculture 

• Agriculture Productivity: (Leader- Shao Yun, Chinese Academy 

of Sciences; Rapporteur- Tamuka Magadzire, USGS/FEWSNET, 

Botswana) 

39


• Water Availability: (Leader- Brad Doorn, National Aeronautics 

and Space Administration, USA; Rapporteur- Estuardo Lira Prera, 

USDA-APHIS-IS, Guatemala) 

• Land Cover and Land Management: (Leader- Anne Smith, 

Agriculture and Agri-Food Canada; Rapporteur- Emil Cherring<strong>to</strong>n, 

CATHALAC, Panama) 

Special no-host dinner, cultural and networking evening (18:30) 

Featured speaker: Robert Sandford (In<strong>to</strong> the Wild: Acts, Accidents & Heroes Who 

have Defined Place in the Rockies) 

Workshop Day 3: Wednesday – 4 November (8:30-17:00) 

8:30-10:00: Breakout 1 (continued) on the technical and operational requirements, 

challenges and strategies for exploitation of <strong>SAR</strong> in agriculture 


10:30-11:00: Reporting on breakout session 

Session Chair: Don Ball (DB Geoservices Inc, Canada) 

Session 4: Bridging the Discovery <strong>to</strong> Delivery Continuum – Capacity 

Building Activities and Opportunities 

Session Chair: Johnson Owaro (GEO Capacity Building Programme 

Secretariat, Uganda) 

11:00-11:20: RADARSAT-2: Science and Operational Applications Research (SOAR) 

Program 

Stéphane Chalifoux (Canadian Space Agency, Canada) 

11:20-11:40: Agri<strong>SAR</strong> 2009 Campaign: Overview, Early Results and Analysis Plan 

Ron Caves (MacDonald Dettwiler and Associates Ltd., Canada) 

11:40-12:10: Developing Earth Observation Agriculture Moni<strong>to</strong>ring Capacity 

Building Programs in Developing Countries: Major Considerations and 

Practices 

Johnson Owaro (GEO Capacity Building Programme Secretariat, 

Uganda) 

12:10-12:30: Capacity Building in Earth Observation Applications for <strong>Agricultural</strong> 

Moni<strong>to</strong>ring in Africa and Central America: A FEWSNET Perspective 

Tamuka Magadzire (FEWS-NET, Botswana) 

40


12:30-13:30: Lunch Break 

Breakout 2: Bridging the Discovery <strong>to</strong> Delivery Continuum – Requirements 

for Transitioning From Research <strong>to</strong> Operational Use and, 

Challenges and Strategies in Capacity Building 

13:30-15:00: Breakout on the requirements for transitioning from research <strong>to</strong> 

operational use, challenges and strategies for exploitation of <strong>SAR</strong> in 

agriculture, capacity building 

• Agriculture Productivity: (Leader- Brian Brisco, Natural 

Resources Canada; Rapporteur- Richard Tswai, ARC-ISCW, 

South Africa) 

• Water Availability: (Ian Jarvis, Agriculture and Agri-Food 

Canada; Rapporteur- ElSayed Hermas, National Authority for 

Remote Sensing and Space Sciences, Egypt) 

• Land Cover and Land Management: (Leader- Anne Smith, 

Agriculture and Agri-Food Canada; Rapporteur- Cynthia Rolli, 

CATHALAC, British Virgin Islands) 


15:20-15:50: Reporting on Breakout Session 2 

Session 5: Panel Discussion 

Session Chair: Chris Justice (University of Maryland, USA) 

15:50-16:20: Strategies for advancing the use of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring; 

recommendations and actions 

Panel members: Gladys Rodriguez Ortega, Marc Thibeault, Trevor 

Taylor, Ian Jarvis, Christiane Schmullius, El Sayed Hermas 

16:20-16:45: Open discussion on the suggested recommendations and actions 

All attendees 

16:45-17:00: Summary, way forward and closing remarks 

Chris Justice, Ken Korporal 

41


Appendix C – Findings of Breakout Sessions 

42

Breakout session 1 & 2 

Technical and operational 

requirements, challenges and 

strategies for exploitation of 

<strong>SAR</strong> in agriculture 

Agriculture Productivity <strong>Group</strong> 

Challenges 

1. data cost (Science & Operations, or S+O) 

2. gap between research & operational users (S+O) 

– Education and technology transfer 

– Development and improved availability of models or methods <strong>to</strong> derive 

information from data 

– Availability of operational software 

3. <strong>SAR</strong> Data (S+O) 

– data sharing based on international standards (OGC etc) 

– improved timeliness of satellite data access 

– Improved data continuity 

– Easier data discovery and order process (e.g. global portal) (S+O) 

– Access <strong>to</strong> geometrically corrected and ortho-rectified or ready-<strong>to</strong>-use 

data (O) 

– Better utilization of archived data and existing systems (S+O) 

4. Development 

– new ‘moni<strong>to</strong>ring focussed’<strong>SAR</strong> systems optimized for specific 

applications with improved design of future systems (S+O) 

– geospatial data assimilation and integration

Solutions/Actions and Suggested Lead 

1. data cost (Science and Operations) 

• Encourage data providers <strong>to</strong> provide access <strong>to</strong> new 

and archived data for research projects, at no or 

minimal cost (GEO and space agencies) 

• Encourage international space agencies <strong>to</strong> consider 

data exchanges <strong>to</strong> meet the needs of the agriculture 

community (GEO and space agencies) 

• Coordinate data procurement <strong>to</strong> take advantage of 

economies of scale (GEO and space agencies) 



•Establish a website portal that includes training on 

<strong>SAR</strong> methods and best practices for agriculture 

applications, including manuals, glossary of terms, 

list of experts, user forums and links <strong>to</strong> <strong>SAR</strong> data 

providers and other resources (GEO) 

•Through the GEO web site, provide links <strong>to</strong> national 

and international announcements of opportunities 

and other funding opportunities (GEO Agriculture 

Community of Practice) 

•Enhance scientific exchanges through annual/biannual 

agriculture Workshops (GEO)



• Develop an inven<strong>to</strong>ry of international science activities and 

<strong>SAR</strong> experts (Research Institutions) 

• Target special journal and book publications <strong>to</strong> disseminate 

state of the art science, specific for agriculture (Research 

Institutions) 

• Document best practices or approaches <strong>to</strong> designing area 

sample frameworks, data pre-processing and processing, 

methods and accuracy assessments related <strong>to</strong> <strong>SAR</strong> and 

agriculture (GEO and Research Institutions) 

• Develop an agricultural <strong>SAR</strong> data community of practice (GEO) 

• Establish international super sites <strong>to</strong> facilitate research 

collaborations (JECAM) 


3. <strong>SAR</strong> Data (S+O) 

•Develop a consensus on data policy needs 

for agriculture and design a strategy <strong>to</strong> 

negotiate liberal data policies (GEO and 

space agencies) 

•Create an international team <strong>to</strong> assess 


design approaches <strong>to</strong> inter-calibrate among 

sensors (GEO and space agencies)


4. Development 

• Encourage the creation of <strong>SAR</strong> satellite 

constellations <strong>to</strong> improve data continuity, revisit, 

provide consistency and prevent redundancy 

(Space agencies) 

• Influence existing and new <strong>SAR</strong> sensor 

development by documenting the requirements of 

the agriculture community with respect <strong>to</strong> Spatial 

resolution, Spatial coverage, Temporal revisit/relook, 

Data latency/delivery, <strong>SAR</strong> sensor parameters 

(frequency, polarizations, angles) (Research 

institutions and Space agencies)

SOILS AND WATER AVAILABILITY 

Break-out 1 & 2 

Science & Operational Challenges 

• Access 

– Reduced data cost 

– Improved data policies (e.g. data access, data continuity) 

– Easier data discovery and order process (e.g. global portal) 

– Improved timeliness of satellite data access 

– Greater access <strong>to</strong> new acquisitions 

– Greater access <strong>to</strong> his<strong>to</strong>rical data archives 

– Coordination <strong>to</strong> existing in-situ data 

• Satellite Configuration 

– Improved spatial (swath) coverage 

– Higher spatial (pixel) resolution 

– Desired <strong>SAR</strong> frequency (wavelength) available 

– Desired <strong>SAR</strong> polarization available 

– Improved revisit/re-look of satellite passes 

• Tool Development 

– Improved availability of models or methods <strong>to</strong> derive information from data 

– Improved data interoperability or integration 

• Policy of commitment <strong>to</strong> data continuity 

• Reduce gap between research & operational users 

– Integration of expertise and knowledge domain specific with Remote Sensing (e.g. soil + hydrology) 

– Community awareness of <strong>SAR</strong> data 

• Research 

– Global Representation (i.e. lack of research on arid regions) 

– Development of backscatter models

Break-out 1 & 2, Science & Operational 

Challenges (1/2) 

Challenge Solution (s) Action/ 

Next Steps 

Access 

Satellite 

Configuration 

Tool Development 

<strong>SAR</strong> Data Buy 

Data Sharing policy 

More satellites and coordinated data 

capture 

Global database with the in-situ data 

Defining and Documenting the 

requirements of the agriculture 

community with respect <strong>to</strong>: 

Spatial resolution 

Spatial coverage 

Temporal revisit/re-look 

Data latency/delivery 

<strong>SAR</strong> sensor parameters (frequency, 

polarizations, angles) 

Provide access <strong>to</strong> <strong>to</strong>olbox <strong>to</strong>gether 

with the data. 

Stimulating the development of the 

<strong>to</strong>ols and its marketing. 

Promote educational software 

Training component in general and 

specific oriented <strong>to</strong> soil and water 

applications. 

Develop end user 

community 

Use the experience of 

successfully communities 

(i.e. weather community) 

Capacity building on 

developing countries (i.e. 

JECAM, SERVIR). 

End user assessment 

oriented <strong>to</strong> regional 

requirements 

Inven<strong>to</strong>ry of training and 

existing <strong>to</strong>ols. 

Regional approach, Global 

initiative (i.e. multilingual) 

Owner/Lead 

GEO 

Space Agencies 

Regional Organizations 

(Agriculture Departments) 

International Water Research 

Institute 

CGIAR 


Soil Community 

Regional Communities 

Universities 

All Space Agencies 

Break-out 1 & 2, Science & Operational 

Challenges (2/2) 

Challenge Solution (s) Action/ 

Next Steps 

Policy of 

commitment <strong>to</strong> 

data continuity 

Reduce gap 

between research 

& operational 

users 

Research 

Government + Commercial 

engagement, considering developed 

and developing countries. 

Represent all the <strong>SAR</strong> Capabilities in 

the policy (i.e. L-Band) 

Integration of expertise and 

knowledge domain specific 

with Remote Sensing (e.g. 

soil + hydrology) 

Community awareness of <strong>SAR</strong> data 

Training 

Improve JECAM experiences. 

Stimulate students <strong>to</strong> take <strong>SAR</strong> 

related careers. 

Engage professional societies. 

End user commitment 

Demonstrations <strong>to</strong> End 

Users 

Extensions Outreach 

Program 

Promotion at High school 

level (similar <strong>to</strong> GIS days) 

Look at educational existing 

programs <strong>to</strong> include <strong>SAR</strong>. 

Engage EO 

Owner/Lead 

GEO 

UN 


GEO 

Universities 

3 

4

LAND MANAGEMENT 

Break-out 1 & 2 

Science & Operational Challenges 

Improvements Needed <strong>to</strong> Make Greater Use 

of <strong>SAR</strong> Data –Science & Operational 

1. Reduced data cost / Improved data policies (e.g. data sharing)** – 

Subsidize data <strong>to</strong> enable free / reduced cost data distribution – 

Identify / secure funding <strong>to</strong> pay for data / services; look at existing 

user payment models such as in met community; coordinate 

exchanges between agencies; User community communicates / 

lobbies data needs <strong>to</strong> space agencies –Operational centers 

requiring data, user Govt agencies; Ag CoP (17) 

2. Lack of training opportunities (at different levels) and awareness – 

Access <strong>to</strong> and development of training courses; online courses / 

materials; combination of regional / local workshops; ‘training of 

trainers’; GEO disseminates training opportunities; greater access <strong>to</strong> 

software –GEO, CEOS, Universities (12) 

3. Improved availability of models or methods <strong>to</strong> derive information 

from data (Lack of best practices re: potential applications of <strong>SAR</strong>) – 

Interoperability between software makers; development of widely 

available algorithms (e.g. in commercial packages) more research in 

developing new, valid algorithms - Ag CoP, Universities (11) 

4. Reduce gap between research & operational users** –Closer 

interactions between research / operational communities; financial 

support for converting research in<strong>to</strong> operations; outreach <strong>to</strong> 

decision-makers; development of widely available algorithms (e.g. in 

commercial packages) –Lobby govts <strong>to</strong> develop policies <strong>to</strong> 

transition research in<strong>to</strong> operations –Donor agencies, Governments 

(11)

Improvements Needed <strong>to</strong> Make Greater Use 

of <strong>SAR</strong> Data –Science & Operational 

5. Improved revisit/re-look of satellite passes (improved temporal 

coverage) –Constellation approach with operational satellites – 

multi-frequency / multi-res –Launch of more satellites –Space 

agencies / Governments (8) 

6. Accessibility of <strong>SAR</strong> polarization / frequency–Lobby for adequate 

mission design; defining globally-distributed ‘super-sites’ 

(requirement depends on operational / scientific uses); 

experimentation with compact pol technology –CEOS, JECAM (8) 

7. Greater access <strong>to</strong> his<strong>to</strong>rical / current data archives - Awareness of 

existing catalogs; relaxing data policies on archived data (EO data 

has shelf life, after which data should simply be made freely 

available) –Process his<strong>to</strong>rical data <strong>to</strong> make available <strong>to</strong> users; User 

community communicates data needs <strong>to</strong> space agencies; open 

announcements of opp. –Ag CoP; Space agencies (4) 

8. Greater access <strong>to</strong> new acquisitions / flexibility in programming 

(dependent on mission type) –Different satellites operating on 

fixed-schedule tasking and flexible-tasking operational models – 

Launch more satellites; coordination between space agencies – 

CEOS (1) –training, <strong>to</strong>ols, data, etc… 

9. Easier data discovery and order process (e.g. global portal) (S) – 

Awareness of existing catalogs (e.g. thru GEO) –list of 10 URLs of 

key portals –GEO, Don Ball (0) 

** - Did not categorize based on science vs operations

Breakout session 3 

Requirements for Transitioning 

from Research <strong>to</strong> Operational Use, 

and Challenges and Strategies for 

Capacity Building 

Agriculture Productivity <strong>Group</strong> 

Unfortunately, the file from this session was 

lost, although the findings were captured in 

the meeting notes and reflected in the report.






Soil & Water Availability <strong>Group</strong> 

Challenge Solution Action/ 

Infrastructures 

•Internet capacity 

•Human capacities 

•Institutional 

capacity 

Data 

•Mode of purchase 

•Cost 

•in situ data 

•Data 

interoporability 

Tools 

•Software 

•Hardware 

•Complementary 

<strong>to</strong>ols (Field <strong>to</strong>ols) 

•Accessing 

technical support 

Owner/Lead 

Recommended Next Steps Approach 

Break-out 3, Capacity Building 

•International support 

for Internet capacities 

•Centre of excellence 

•Training of trainers 

•Regional Node of 

recieving stations 

•Data buy 

•Network of in-situ 

data 

•Data standards 

pro<strong>to</strong>cols 

•Grants for 

purchasing hardware 

and software and 

Field <strong>to</strong>ols 

•Open Source 

software 

•Scientific aid 

•Analysis of 

Capacity gaps 

• 

•Inven<strong>to</strong>ry of… 

various issues 

• 

•Assessment of 

user needs 

•Inven<strong>to</strong>ry of free 

SW 

•Lobby Aid 

agencies 

• 

•Community 

ofpractice with the 

Geo-Capacity 

•GEO with space 

agencies 

•Community of 

practice 

•Community 

practice with the 

end users 

•GEO

Challenge 

Training 

•Cost of training 

•Location of 

training 

•Langauge 

•Relevancy 

Solution 

•Demo of utllity of 

data application 

•Online training 

•Mulit-lingual 

regionally appropriate 

training 

•Thematically relavant 

training 

•High school levels 

through Univ 

Action/ 

Next Steps 

•Assessment of 

training needs 

•Inven<strong>to</strong>ry of 

training resources 

and gaps 

•Development of 

regional strategies 

Owner/Lead 

•GEO 

•Universities and 

research 

institutions






Land Management <strong>Group</strong> 


Next Steps 

Acquiring 

knowledge <strong>to</strong> use 

the data and 

understanding the 

application of the 

data 

•Commercial, 

operational <strong>to</strong> use or 

initiate programs 

similar <strong>to</strong> EOADP 

•Ability <strong>to</strong> teach 

courses with relevant 

data, necessary <strong>to</strong> 

ensure success of the 

course 

•Enhance cooperation 

amongst the data 

providers and 

researchers; user 

community 

•Funding for 

programs; lobbying 

•Funding for training, 

•Invest in the 

universities <strong>to</strong> 

develop a program <strong>to</strong> 

provide sustainable 

training 

•Keep programs 

such as SOAR 

open on an ongoing 

basis, for 

example Terra<strong>SAR</strong> 

•International 

Symposium on 

remote sensing in 

Agriculture 

•Identify available 

funding projects 

through 

international 

developing 

agencies 

Owner/Lead 

•GEO 

•Space Agencies 

•Development 

Agencies 

•Universities 

•User communities


Next Steps 

Acquiring 

knowledge <strong>to</strong> use 

the data and 

understanding the 

application of the 

data (continued) 

Cost of Data 

•Establishing 

programs that could 

provide data <strong>to</strong> 

developing countries 

•Augment data for use 

with free software 

•Develop and 

implement public 

outreach programs 

in schools and 

universities 

•Initiate ‘<strong>SAR</strong> Day’ 

as an educational 

outreach 

opportunity 

•Multi-media 

outreach products; 

video clips 

Replicate existing 

programs (GMES) 

<strong>to</strong> supply data <strong>to</strong> 

developing 

countries 


Next Steps 

Managerial 

support/political 

support 

•Develop cost-benefit 

product <strong>to</strong> inform 

decision makers 

•Present the 

information product 

•Communication of 

the societal benefits 

from the 

scientists/researchers 

<strong>to</strong> the decision 

makers and funding 

agencies and 

industries {insurance 

agencies} 

•Demonstration of 

benefits <strong>to</strong> policy 

makers 

Derive 

demonstration 

products <strong>to</strong> prove 

benefits 

Share demo 

products (success 

s<strong>to</strong>ries) via ‘Geo 

Portal’ 

Owner/Lead 

Space and 

development 

agencies 

Owner/Lead 

Researchers, 

developers, endusers, 

GEO


Next Steps 

Software; 

availability and ease 

of use, different 

levels for user’s 

•Availability of free 

software solutions 

•Reduced price for 

universities 

•Develop additional 

and enhanced <strong>to</strong>ols 

for data users 

•Provide reduced 

cost for software 

with training 

programs 


agencies sponsor 

licenses for 

software 

Owner/Lead 

•Software 

developers 

•Space Agencies 


Agencies


Appendix D – Alaska Satellite Facility: Data Portal, <strong>SAR</strong> 

Tools, and <strong>SAR</strong> Training 

The Alaska Satellite data portal for data is available at http://www.asf.alaska.edu/ 

ALOS: Researchers and commercial users in the Americas can access ALOS data (optical 

and <strong>SAR</strong>) for any region in the world. For researchers, all data is $125 US per scene. 

Commercial prices depend on the product. 

JERS-1, ERS-1, ERS-2, AIR<strong>SAR</strong>, RADARSAT-1: U.S. researchers and their 

international collabora<strong>to</strong>rs may access archival data and new ERS-1data by submitting a 

brief proposal. Data will be provided free of charge. 

In all cases, users may order <strong>SAR</strong> data in the following modes: Level 0, Single Look 

Complex (SLC), or image products. 

The MapReady Tool Kit is accessible at 

http://www.asf.alaska.edu/sardatacenter/software<strong>to</strong>ols 

This <strong>to</strong>ol ingests <strong>SAR</strong> data in its native format, performs terrain correction, geocodes <strong>to</strong> 

various map projections, and exports products as GeoTIFFs. In addition, MapReady now 

supports various polarimetric decompositions and operates in a complementary way with 

Pol<strong>SAR</strong>pro. Specifically, it supports the conversion of Pol<strong>SAR</strong>pro products <strong>to</strong> terraincorrected 

GeoTIFFs, permitting its use in GIS. 

Pol<strong>SAR</strong>pro can be downloaded from http://earth.esa.int/polsarpro/default.html 

ASF offers <strong>SAR</strong> and polarimetry courses: 

26 April, 2010: Half-day workshop on the polarimetric classification of land cover at the 

ASPRS Meeting in San Diego, California. 

June, 2010: Week-long course on <strong>SAR</strong>: Theory and Applications, hosted at the University 

of Alaska Fairbanks. 

<strong>SAR</strong> courses can be organized in response <strong>to</strong> request for groups of up <strong>to</strong> 20 students. Cost 

is based on materials and transportation of instruc<strong>to</strong>r. 

43


Appendix E – Workshop Organizing Committee 

Chair - Kenneth Korporal (CGEO Secretariat) 

Michael Brady (Natural Resources Canada) 

Brian Brisco (Natural Resources Canada) 

Catherine Champagne (Agriculture and Agri-Food Canada) 

Jason Duffe (Environment Canada) 

Greg Gibbons (Agriculture and Agri-Food Canada) 

Ian Jarvis (Agriculture and Agri-Food Canada) 

John Linn (Canadian Space Agency) 

Heather McNairn (Agriculture and Agri-Food Canada) 

Erin Naydenov (Natural Resources Canada) 

Anna Pacheco (Agriculture and Agri-Food Canada) 

Bob Parkinson (Agriculture and Agri-Food Canada) 

Gordon Reichert (Statistics Canada) 

Kerri Swail (CGEO Secretariat) 

Bliss Tracy (Health Canada) 

Grant Wiseman (Agriculture and Agri-Food Canada) 

44


Appendix F – International Technical Team 

Co-Chair - Heather McNairn (Agriculture and Agri-Food Canada) 

Co-Chair - Jai Parihar (India Space Applications Centre) 

Brian Brisco (Natural Resources Canada) 

Catherine Champagne (Agriculture and Agri-Food Canada) 

Michael Collins (University of Calgary) 

Yves Crevier (Canadian Space Agency) 

Craig Dobson (National Aeronautics and Space Administration) 

Naser El-Sheimy (University of Calgary) 

Irena Hajnsek (German Aerospace Centre) 

Kenneth Korporal (CGEO Secretariat) 

Susan Moran (United States Department of Agriculture) 

Rick Mueller (United States Department of Agriculture) 

Johnson Owaro (Government of Uganda) 

Anna Pacheco (Agriculture and Agri-Food Canada) 

Genya Sai<strong>to</strong> (Tohoku University) 

Bill Salas (Applied GeoSolutions) 

Anne Smith (Agriculture and Agri-Food Canada) 

Bliss Tracy (Health Canada) 

Zhengwei Yang (United States Department of Agriculture) 

Shao Yun (Chinese Academy of Sciences) 

45


Appendix G – List of Poster Presentations 

46


47


Appendix H – List of Participants 

48

Country 

State or 

Province 

Title Last Name First Name Name on Badge Organization Job Title City 

Mr Akanni Adeniran Akanni Adeniran Ministry of Environment Scientific Officer Lagos Lagos NIGERIA 

Mr Al-Bakri Jawad Jawad Al-Bakri University of Jordan Associate Professor Amman JORDAN 

Mr Atwood Don Don Atwood Alaska Satellite Facility Chief Scientist Fairbanks Alaska USA 

Mr Auger Denis Denis Auger Canadian Space Agency St. Hubert Quebec CANADA 

Mr Ball Don Don Ball DB Geoservices Inc President Ottawa Ontario CANADA 

Agriculture Financial Services 

Risk Analyst Lacombe Alberta CANADA 

Corporation 

University of Maryland Faculty Research Assistant College Park Maryland USA 

Mr Basnyat Prakash Prakash Basnyat 

Inbal Becker- 

Reshef 

Ms Becker-Reshef Inbal 

BELGIQUE 

Brabant 

Wallon 

Louvain-la- 

Neuve 

Mme Beriaux Emilie Emilie Bériaux Universite Catholique de Louvain Assistante de recherche 

Mr Brady Michael Michael Brady NRCAN - CFS Executive Direc<strong>to</strong>r GOFC-GOLD Edmon<strong>to</strong>n Alberta CANADA 

Edmon<strong>to</strong>n Alberta CANADA 

Direc<strong>to</strong>r - Environmental 

Stewardship Division 

Alberta Agriculture and Rural 

Development 

Mrs Brindle Brenda Brenda Brindle 

Mr Brisco Brian Brian Brisco CCRS - NRCAN RES Ottawa Ontario CANADA 

CANADA 

British 

Columbia 

Mr Caves Ron Ron Caves MDA Senior Analyst Richmond 

Stephane 

Mr Chalifoux Stephane 

Canadian Space Agency Manager, RADARSAT-2 Allocation Longueuil Quebec CANADA 

Chalifoux 

Catherine 

Remote Sensing Applications 

Ms Champagne Catherine 


Ottawa Ontario CANADA 

Champagne 

Development 

Francois 

Mr Charbonneau Francois 

Canada Centre for Remote Sensing Research Scientist Ottawa Ontario CANADA 

Charbonneau 

Saskatche 

Ms Chen Chun Chun Chen Digital Environmental Remote Sensing Specialist Saska<strong>to</strong>on 

CANADA 

wan 

Mr Cherring<strong>to</strong>n Emil Alexander Emil. A Cherring<strong>to</strong>n CATHALAC Senior Scientist Panama PANAMA 

Mr Collins Michael Michael Collins University of Calgary Professor Calgary Albet CANADA 

Mr Cotlier Carlos Carlos Cotlier Universidad Nacional de Rosario Direc<strong>to</strong>r of Remote Sensing Center Rosario ARGENTINA 

Earth Observation Applications 

Mr Crevier Yves Yves Crevier Canadian Space Agency 

St. Hubert Quebec CANADA 

and Utilization 

Soil Moisture Map SAOCOM 

Mr Dadamia Danilo Jose Danilo J. Dadamia CONAE 

Buenos Aires ARGENTINA 

Project 

Dr. Roshanak 

Mrs Darvishzadeh Roshanak 

Shahid Beheshti University Assistant Professor Tehran IRAN 

Darvish 

Mr Davidson Malcolm Malcolm Davidson ESA GERMANY 

Argentine National Institute of 

Buenos 

Mr de Abelleyra Diego Diego de Abelleyra Researcher Hurlingham 

ARGENTINA 

<strong>Agricultural</strong> Technology 

Aires 

Mr Diaz Ponce Alejandro Alejandro Diaz National Water Commission Geographer Mexico City MEXICO

Country 

State or 

Province 


Mr Doorn Bradley Brad Doorn NASA Program Scientist Washing<strong>to</strong>n DC USA 

Mr Duffe Jason Jason Duffe Environment Canada Geomatics Specialist Ottawa Ontario CANADA 

Mr Erickson Scott Scott Erickson Agriculture and Agri-Food Canada Research Technician Lethbridge Alberta CANADA 

Mr Fan Jinlong Jinlong Fan GEO Secretariat Geneva SWITZERLAND 

Yolanda 

Ms Fernandez-Ordonez Yolanda Fernandez- Colegio de Postgraduados Professor Texcoco MEXICO 

Ordonez 

Saskatche 

Ms Finnigan Carmen Carmen Finnigan Digital Environmental Remote Sensing Specialist Saska<strong>to</strong>on 

CANADA 

wan 

Saskatche 

Mr Finnigan James Jim Finnigan Digital Environmental President Saska<strong>to</strong>on 

CANADA 

wan 

Mr Flores Rafael Rafael Flores CONAFOR National Forestry Inven<strong>to</strong>ry Zapopan MEXICO 

Ms Frank Grace Grace Frank Agriculture and Agri-Food Canada Geomatics Coordina<strong>to</strong>r Summerland BC CANADA 

Mr Geng Xiaoyuan Xiaoyuan Geng Agriculture and Agri-Food Canada Soil Scientist Ottawa Ontario CANADA 

Mr Gibbons Greg Greg Gibbons Agriculture and Agri-Food Canada GIS <strong>Support</strong> Ottawa Ontario CANADA 

Mr Grimes David David Grimes Environment Canada Associate Deputy Minister Ottawa Ontario CANADA 

National Authority for Remote Sensing Remote Sensing Application 

Mr Hermas ElSayed ElSayed Hermas 

Cairo EGYPT 

and Space Sciences 

Scientist 

Agriculture Financial Services 

Mr Hildebrand David David Hildebrand Project Manager Lacombe Alberta CANADA 

Corporation 

Indian Head <strong>Agricultural</strong> Research 

Saskatche 

Mr Holzapfel Chris Chris Holzapfel 

Researcher Indian Head 

CANADA 

Foundation 

wan 

Mr Hou Ming Ming Hou Northwest A&F University Joint-training PhD candidate Ottawa Ontario CHINA 

National Institute for Agro- 

Dr Ishitsuka Naoki Naoki Ishitsuka 

Senior Researcher Ibaraki JAPAN 

Environmental Sciences 

Alberta Agriculture and Rural 

Mr Itenfisu Daniel Daniel Itenfisu 

Drought Modeler Edmon<strong>to</strong>n Alberta CANADA 

Development 

Falda del 

Mr Izaurralde Jorge Jorge Izaurralde CONAE <strong>SAR</strong> Applications (SAOCOM) 

Cordoba ARGENTINA 

Carmen 

Mr Jarvis Ian Ian Jarvis Agriculture and Agri-Food Canada Earth Observation Manager Manotick Ontario CANADA 

Mr Jeronimus Eric Eric Jeronimus MDA Federal Geospatial Analyst Rockville Maryland USA 

Institute of Space and Earth 

Mr Jinsong Chen Chen Jingsong 

Assistant Professor Hong Kong Hong Kong CHINA 

Information Science, CUHK 

Dode Bendu Regional Centre for Training in 

Mr Johnson Dode 

Lecturer Ile-Ife Osun NIGERIA 

Johnson Aerospace Surveys 

Marketing and Product 

Mr Jones Kevin Kevin Jones PCI Geomatics Enterprises 

Gatineau Quebec CANADA 

Development 

Mr Justice Chris Chris Justice University of Maryland Professor College Park Maryland USA 

Ms Kasuga Akiko Akiko Kasuga ImageONE Co. Ltd. Sales and Marketing Shinjyuku-ku JAPAN

Country 

State or 

Province 


Mr Korporal Kenneth Ken Korporal Canadian GEO Secretariat Coordina<strong>to</strong>r Ottawa Ontario CANADA 

Beira Sofala MOZAMBIQUE 

Direc<strong>to</strong>r - Centre for Geographic 

Information 

Mr Kwesha Dominick Dominick Kwesha Catholic University of Mozambique 

Mr Lamparelli Rubens Augus<strong>to</strong> Rubens Lamparelli University of Campinas Researcher Campinas Sao Paolo BRAZIL 

Miss Leblon Brigitte Brigitte Leblon UNB Professor Frederic<strong>to</strong>n NB CANADA 

Estuardo Lira 

Mr Lira Prera Estuardo 

USDA-APHIS-IS-Medfly Program GIS Specialist Guatemala GUATEMALA 

Prera 

Mr Liu Feng Feng Liu Agriculture and Agri-Food Canada Visiting scholar Ottawa Ontario CANADA 

Homero 

Buenos 

Mr Lozza 

Homero F. Lozza CONAE System Engineer Ezeiza 

ARGENTINA 

Fernando 

Aires 

Tinomutenda 

Mr Magadzire 

Tamuka Magadzire USGS/FEWSNET Southern Africa Regional Scientist Gaborone BOTSWANA 

Tamuka 

Humber<strong>to</strong> Martinez 

Mr Martinez Humber<strong>to</strong> 

SIAP Mexico City MEXICO 

Roman 

Mr Mattia Francesco Francesco Mattia National Council of Research Research Scientist Bari Ba ITALY 

Dr. Alex 

Mr Melnitchouck Alex 

DynAgra Corp. Senior Research Agronomist Beiseker Alberta CANADA 

Melnitchouck 

Mr Mercer Bryan Bryan Mercer Intermap Technologies Corp. Chief Scientist Calgary Alberta CANADA 

Agriculture and Agri-Food Canada PDF Ottawa Ontario CANADA 

Dr. Amine 

Merzouki 

Rolando 

Montenegro 

Mr Merzouki Amine 

Universidad del Valle de Guatemala Engineer Forester Guatemala GUATEMALA 

Mr Montenegro Rolando 

Deputy Direc<strong>to</strong>r Almaty KAZAKHSTAN 

National Center of Space Researches 

and Technologies 

Mrs Mura<strong>to</strong>va Nadiya Nadiya Mura<strong>to</strong>va 

Mrs Naydenov Erin Erin Naydenov Natural Resources Canada Forest Officer Edmon<strong>to</strong>n Alberta CANADA 

Mr Nelson Sherman Sherman Nelson Agriculture and Agri-Food Canada Direc<strong>to</strong>r - Agri-Geomatics Ottawa Ontario CANADA 

Dr. Nathaniel K. 

Mr Newlands Nathaniel 

Agriculture and Agri-Food Canada Scientist Lethbridge Alberta CANADA 

Newlands 

Mr Niang Mohamed M. A Niang Agriculture and Agri-Food Canada Visiteur scientifique Ste-Foy Quebec CANADA 

Saskatche 

Mr O'Brien Ted Ted O'Brien Environment Canada Manager, National Service Office Regina 

CANADA 

wan 

Mr Ohki Masa<strong>to</strong> Masa<strong>to</strong> Ohki JAXA Engineer Tsukuba Ibaraki JAPAN 

Kampala Central UGANDA 

Coordina<strong>to</strong>r for Disaster 

Preparedness 

Remote Sensing Research 

Specialist 

Mr Owaro Johnson Johnson Owaro Office of the Prime Minister 


Mrs Pacheco Anna Anna Pacheco Agriculture and Agri-Food Canada 

Mr Parihar Jai Jai S. Parihar Space Applications Centre (ISRO) Direc<strong>to</strong>r Ahmedabad Gujarat INDIA 

Mr Patnaik Chakrapani C. Patnaik Space Applications Centre (ISRO) Scientist Ahmedabad Gujarat INDIA 

Miss Picoli Michelle Michelle Picoli UNICAMP P.h.D. Student Campinas Sao Paolo BRAZIL

Country 

State or 

Province 


Ms Podest Erika Erika Podest JPL-NASA Scientist Pasadena California USA 

Mr Poitevin Jean Jean Poitevin Parks Canada Applied Research Coordina<strong>to</strong>r Gatineau Quebec CANADA 

Mr Pomeroy John John Pomeroy University of Saskatchewan Professor Canmore Alberta CANADA 


Chief, Remote Sensing and 

Geospatial Analysis 

Mr Reichert Gordon Gordon Reichert Statistics Canada 

Mr Rivera Olivar David Rivera SIAP Pro<strong>to</strong>col Programmer Mexico City MEXICO 

CONAE Buenos Aires ARGENTINA 

Gladys Rodriquez 

Ortega 

Mrs Rodriguez Ortega Gladys 

Department of Disaster Management Senior Technical Planning 

BRITISH VIRGIN 

Mrs Rolli Cynthia Cynthia Rolli 

MacNamara 

(BVI) 

Manager 

ISLANDS 

Mr Rollin Patrick Patrick Rollin Agriculture and Agri-Food Canada G.I.S. Ottawa Ontario CANADA 

Mr Romero Martin Martin Romero INIFAP Research Associate Mexico City MEXICO 

Gustavo A. Rosal 

Mr Rosal Gustavo 

Ministerio de Agricultura GIS Analyst Guatemala GUATEMALA 

Samayoa 

Consejo Nacional de Areas 

Tecnico en Sistemas de 

Mr Rosales Riveiro Kenset Amaury Kenset Rosales 

Guatemala GUATEMALA 

Protegiadas 

Informacion Geografica 

Mr Roth Achim Achim Roth German Aerospace Centre DLR Terra<strong>SAR</strong>-X Science Coordina<strong>to</strong>r Wessling Bavaria GERMANY 

Mr Sabel Daniel Daniel Sabel Vienna University of Technology PhD Student Vienna AUSTRIA 

Mr Sai<strong>to</strong> Genya Genya Sai<strong>to</strong> Tohoku University Professor Sendai Miyagi JAPAN 

Mr Sandford Bob Bob Sandford Canmore Alberta CANADA 

Ms Schmullius Christiana Chris Schmullius University of Jena Head of Department Jena Thuringia GERMANY 

Ms Shang Jiali Dr. Jiali Shang Agriculture and Agri-Food Canada Remote Sensing Specialist Ottawa Ontario CANADA 

Institute of Remote Sensing 

Ms Shao Yun Yun Shao 

Beijing Chaoyang CHINA 

Applications, CAS 

Mr Skriver Henning Henning Skriver Technical University of Denmark Associate Professor Lyngby DENMARK 

Ms Smith Anne Anne Smith Agriculture and Agri-Food Canada Research Scientist Lethbridge Alberta CANADA 

CANADA 

British 

Columbia 

Mr Smith Scott Scott Smith Agriculture and Agri-Food Canada Soil Scientist Summerland 

Mr Soria-Ruiz Jesus Jesus Soria-Ruiz INIFAP Researcher Zinacantepec MEXICO 

Mr Staples Gordon Gordon Staples MDA Project Manager Richmond BC CANADA 

Deputy Direc<strong>to</strong>r of Spatial Data 

Mr Sugardiman Ruandha Agung R.A. Sugardiman Ministry of Forestry 

Jakarta INDONESIA 

Network 

Miss Swail Kerri Kerri Swail Canadian GEO Secretariat Program Analyst Ottawa Ontario CANADA 

Mr Thibeault Marc Marc Thibeault CONAE Investiga<strong>to</strong>r SAOCOM Project Buenos Aires ARGENTINA 

Mr Torbick Nathan Nathan Torbick Applied Geosolutions Durham NH USA 

Mr Tracy Bliss Bliss Tracy Health Canada Head, Radiological Impact Ottawa Ontario CANADA

Country 

State or 

Province 


Richmond Hill Ontario CANADA 

Direc<strong>to</strong>r, Strategic Business 

Development 

Mr Trevor Taylor Trevor Taylor PCI Geomatics Enterprises 

Mr Tswai Richard Richard Tswai <strong>Agricultural</strong> Research Council Remote Sensing Researcher Pre<strong>to</strong>ria Gauteng SOUTH AFRICA 

British 

Mrs Vujanic Gordana Gordana Vujanic MDA Key Account Manager Richmond 

CANADA 

Columbia 

Saskatche 

Mr Waldner David David Waldner Agriculture and Agri-Food Canada Agroclimate Data Analyst Regina 

CANADA 

wan 

Mr Wang Xuejun Xuejin Wang State Forestry Administration Beijing East District CHINA 

Mr Wiseman Grant Grant Wiseman Agriculture and Agri-Food Canada Remote Sensing Specialist Winnipeg Mani<strong>to</strong>ba CANADA 

Mrs. Wiseman Kylene Kylene Wiseman APEGM Profesional Geoscientist Winnipeg Mani<strong>to</strong>ba CANADA 

Mr Xu Maosong Maosong Xu State Forestry Administration Beijing East District CHINA 

Mr Yanan Yin Yin Yanan ImageONE Co. Ltd. Principal Engineer Shinjyuku-ku JAPAN 

David Oscar 

Mr Yawson Oscar David 

University of Cape Coast Lecturer/Researcher Cape Coast GHANA 

Yawson


Annex 1 – Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> 

<strong>Agricultural</strong> Moni<strong>to</strong>ring 

49

International GEO Workshop on Synthetic Aperture Radar (<strong>SAR</strong>) 

<strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Report on Pre-Workshop Survey 

© Government of Canada, 2009

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Executive Summary 

The Canadian <strong>Group</strong> on Earth Observations (C-GEO) joined with the Government of 

Canada and other sponsors <strong>to</strong> organize an International GEO Workshop on <strong>SAR</strong> 

(synthetic aperture radar) <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring in Kananaskis, Alberta on 2 

– 4 November 2009. This Workshop brought <strong>to</strong>gether remote sensing experts and 

resource managers who map agricultural land cover and land use; moni<strong>to</strong>r changes in the 

agricultural land base; identify crops and estimate crop area; qualitatively and 

quantitatively assess crop condition; and moni<strong>to</strong>r properties related <strong>to</strong> water availability 

such as soil moisture. 

The Workshop also identified and communicated the information and operational needs 

of the agricultural user community <strong>to</strong> facilitate coordinated access <strong>to</strong> Earth observation 

data. A “by invitation” training session was organized, targeted <strong>to</strong>wards participants 

from developing countries, and it preceded the Workshop from 31 Oc<strong>to</strong>ber – 1 

November. 


community, including those who planned <strong>to</strong> attend the workshop. The plan was <strong>to</strong> collect 

the views of the community and present this <strong>to</strong> the workshop attendees, <strong>to</strong> ‘prime the 

pump’ and stimulate discussion. The organizers wanted <strong>to</strong> know the current state of 

agricultural moni<strong>to</strong>ring, in particular using <strong>SAR</strong> data, and what the issues were and what 

actions could encourage greater use of <strong>SAR</strong> data for agricultural moni<strong>to</strong>ring. During the 

workshop, people were encouraged <strong>to</strong> fill out the survey if they had not done so already. 

This survey provided an important opportunity for stakeholders <strong>to</strong> express their views on 

the priority strategies <strong>to</strong> facilitate greater use of <strong>SAR</strong> by the agriculture community. A 

report from the Workshop includes an aggregated summary of these survey results. This 

report is widely available and accessible at www.cgeo.gc.ca. 

The survey responses of the sixty-two respondents (the <strong>to</strong>tal received as of early 

December 2009) have been analyzed and reported in their entirety. This information was 

summarized in presentation form and was presented by the author at the International 

GEO Workshop on <strong>SAR</strong> for <strong>Agricultural</strong> Moni<strong>to</strong>ring. 

Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring – Annex 1 i


Acknowledgements 

The author would like <strong>to</strong> thank the following individuals who contributed <strong>to</strong> this report: 

Heather McNairn, Anna Pacheco, Catherine Champagne, Ian Jarvis, Kenneth Korporal, 

Kerri Swail, Erin Naydenov, Janet Kwan, Paul Briand, Brian Brisco and all of the survey 

respondents. 

Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring – Annex 1 ii


Table of Contents 

Executive Summary............................................................................................................. i 

Acknowledgements............................................................................................................. ii 

1. Introduction.................................................................................................................1 

2. Methodology............................................................................................................... 2 

3. The Respondents......................................................................................................... 3 

4. Current and Planned Use of <strong>Agricultural</strong> Moni<strong>to</strong>ring Data........................................ 5 

4.1 Current Needs for <strong>Agricultural</strong> Information....................................................... 5 

4.2 Current and Planned Use of <strong>SAR</strong> Data............................................................. 12 

4.3 Current and Planned Use of Optical Data......................................................... 14 

4.4 Current and Planned Use of In-situ Data .......................................................... 16 

4.5 Information Requirements and Application Status........................................... 19 

5. Views on How <strong>to</strong> Encourage Greater Use of <strong>SAR</strong> Data .......................................... 25 

5.1 Encouraging Greater Use of <strong>SAR</strong> Data for <strong>Agricultural</strong> Moni<strong>to</strong>ring............... 25 

5.2 Encouraging Greater Use of <strong>SAR</strong> Data through Science ................................. 26 

5.3 Encouraging Greater Operational Use of <strong>SAR</strong> Data ........................................ 27 

5.4 Encouraging Greater Capacity <strong>to</strong> Use <strong>SAR</strong> Data ............................................. 29 

5.5 Other Suggestions <strong>to</strong> Encourage Greater Use of <strong>SAR</strong> Data............................. 30 

5.6 Other Views ...................................................................................................... 31 

6. Conclusions............................................................................................................... 33 

Annex A – Questionnaire 

Annex B – Acronyms 

List of Tables 

Table 1 Number of Survey Responses from Each Region or Continent and Country ..... 3 

Table 2 Research/Operational Mix for Each Moni<strong>to</strong>ring Application ............................. 6 

Table 3 Current and Planned Use of <strong>SAR</strong> Data.............................................................. 13 

Table 4 Current and Planned Use of Optical Data.......................................................... 15 

Table 5 Current and Planned Use of In-situ Data........................................................... 16 

Table 6 Information Requirements for Specific Moni<strong>to</strong>ring Applications..................... 19 

Table 7 Fac<strong>to</strong>rs that Encourage Greater Use of <strong>SAR</strong> Data............................................. 25 

Table 8 Fac<strong>to</strong>rs that Encourage Greater Use of <strong>SAR</strong> Data Through Science ................ 27 

Table 9 Fac<strong>to</strong>rs that Encourage Greater Operational Use of <strong>SAR</strong> Data......................... 28 

Table 10 Fac<strong>to</strong>rs that Encourage Greater Capacity <strong>to</strong> Use <strong>SAR</strong> Data............................ 29 

List of Figures 

Figure 1 Types of Organizations Represented.................................................................. 4 

Figure 2 Major Applications of <strong>Agricultural</strong> Moni<strong>to</strong>ring Data ........................................ 5 

Figure 3 Spatial Scales of Crop Production Applications ................................................ 7 

Figure 4 Temporal Scales of Crop Production Applications............................................ 7 

Figure 5 Spatial Scales of Water Availability Applications ............................................. 8 

Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring – Annex 1 iii


Figure 6 Temporal Scales of Water Availability Applications......................................... 9 

Figure 7 Spatial Scales of Land Management Applications........................................... 10 

Figure 8 Temporal Scales of Land Management Applications ...................................... 10 

Figure 9 Spatial Scales of Ecosystem Health Applications............................................ 11 

Figure 10 Temporal Scales of Ecosystem Health Applications...................................... 11 

Figure 11 Approximate Number of <strong>SAR</strong> Scenes Used per Year.................................... 14 

Figure 12 Ratio of Applications at Varying Stages ........................................................ 23 

Figure 13 Importance of <strong>SAR</strong> Data for <strong>Agricultural</strong> Moni<strong>to</strong>ring Applications ............. 24 

Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring – Annex 1 iv


1. Introduction 

The Canadian <strong>Group</strong> on Earth Observations (C-GEO) joined with the Government of 

Canada and other sponsors <strong>to</strong> organize an International GEO Workshop on <strong>SAR</strong> 

(synthetic aperture radar) <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring in Kananaskis, Alberta on 2 

– 4 November 2009. This Workshop brought <strong>to</strong>gether remote sensing experts and 

resource managers who map agricultural land cover and land use; moni<strong>to</strong>r changes in the 

agricultural land base; identify crops and estimate crop area; qualitatively and 

quantitatively assess crop condition; and moni<strong>to</strong>r properties related <strong>to</strong> water availability 

such as soil moisture. 

The Workshop also identified and communicated the information and operational needs 

of the agricultural user community <strong>to</strong> facilitate coordinated access <strong>to</strong> Earth observation 

data. A “by invitation” training session was organized, targeted <strong>to</strong>wards participants 

from developing countries, and it preceded the Workshop from 31 Oc<strong>to</strong>ber – 1 

November. 

The goal of the Workshop was <strong>to</strong> state clearly and strongly the Earth Observation 

requirements of the international agriculture community and the strategies needed <strong>to</strong> 

promote the use of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring, in the context of science, research, 

operations and capacity. 

The specific objectives of the Workshop were <strong>to</strong>: 

1. Increase understanding of the capabilities of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring; 

2. Share best practices with respect <strong>to</strong> processing, analysis and modeling of <strong>SAR</strong> 

data for agricultural mapping and moni<strong>to</strong>ring; 

3. Increase awareness of software <strong>to</strong>ols, applications and processes for the effective 

use of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring; 

4. Build capacity within the agricultural user community <strong>to</strong> exploit <strong>SAR</strong> data, 

especially in developing countries; and 

5. Initiate a dialogue <strong>to</strong> identify the needs of the agriculture moni<strong>to</strong>ring community 

with <strong>SAR</strong> satellite opera<strong>to</strong>rs and <strong>SAR</strong> application software developers. 


community, including those who planned <strong>to</strong> attend the workshop. The plan was <strong>to</strong> collect 

the views of the community and present this <strong>to</strong> the workshop attendees, <strong>to</strong> ‘prime the 

pump’ and stimulate discussion. The organizers wanted <strong>to</strong> know the current state of 

agricultural moni<strong>to</strong>ring, in particular using <strong>SAR</strong> data, and what the issues were and what 

actions could encourage greater use of <strong>SAR</strong> data for agricultural moni<strong>to</strong>ring. During the 

workshop, people were encouraged <strong>to</strong> fill out the survey if they had not done so already. 

This survey provided an important opportunity for stakeholders <strong>to</strong> express their views on 

the priority strategies <strong>to</strong> facilitate greater use of <strong>SAR</strong> by the agriculture community. A 

report from the Workshop includes an aggregated summary of these survey results. This 

report is widely available and accessible at www.cgeo.gc.ca. 

Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring – Annex 1 1


2. Methodology 

At the kick-off meeting, the project team discussed the information requirements, in other 

words, what information we really need <strong>to</strong> get from this survey. Based on the 

information requirements, the survey was drafted and discussed within the team. The 

survey is provided in Appendix C. The survey was divided in<strong>to</strong> three main sections: 

• Information about the respondent, description of their organization and role in the 

organization. 

• Current and planned use of agricultural moni<strong>to</strong>ring data: information needs by 

application, types and amount of <strong>SAR</strong> data used, types of optical data used, in-situ 

data types, technical information requirements, maturity of moni<strong>to</strong>ring 

applications and importance of <strong>SAR</strong> data in their moni<strong>to</strong>ring applications. 

• Views on How <strong>to</strong> Encourage Greater Use of <strong>SAR</strong> Data: fac<strong>to</strong>rs that would 

encourage greater use of <strong>SAR</strong> data for agricultural moni<strong>to</strong>ring, suggestions <strong>to</strong> 

encourage: 

o greater use of <strong>SAR</strong> data through science, 

o greater operational use of <strong>SAR</strong> data and 

o greater capacity <strong>to</strong> use <strong>SAR</strong> data. 

The survey was drafted in a ‘clickable’ format by Agriculture and Agri-Food Canada 

(AAFC) in consultation with the C-GEO. An introduction letter was drafted <strong>to</strong> inform 

stakeholders about the survey and entice them <strong>to</strong> send back the filled in survey. 

A small sample of stakeholders was asked <strong>to</strong> fill in a ‘test’ version of the survey. Based 

on their feedback, the survey was finalized. Then it was translated and e-mailed out with 

the introduction letter by the C-GEO office. 

The survey was distributed via: 

• mailing list, 

• the C-GEO web site, and 

• the encouragement of regional agricultural representatives. 

The C-GEO office sent out a reminder e-mail <strong>to</strong> the mailing list. In addition, participants 

at the workshop were encouraged <strong>to</strong> fill out the survey. 

The author drafted a spreadsheet <strong>to</strong> capture survey results such that statistical analysis 

could be done efficiently. Once the questionnaire replies came in, they were copied in<strong>to</strong> 

an analysis spreadsheet by personnel from the Canadian Forest Service (CFS). 

The author then summarized all of the survey results in this report. 



3. The Respondents 

This section describes the sixty-two respondents (the <strong>to</strong>tal received as of early December 

2009) <strong>to</strong> the survey. 26% of them are female and 74% male. The number of survey 

responses from each country is shown in Table 1. 

Table 1 Number of Survey Responses from Each Region or Continent and Country 

Region or Continent # of 

Responses 

North America 23 37% 

Latin America 8 13% 

Asia 

Middle East 

Europe 

Africa 

12 

3 

8 

8 

% of Country # of 



19% 

5% 

13% 

13% 

% of 


Canada 17 27.4% 

USA 6 9.7% 

Mexico 1 1.6% 

Cuba 1 1.6% 

Brazil 2 3.2% 

Argentina 3 4.8% 

Panama 1 1.6% 

China 4 6.5% 

Indonesia 1 1.6% 

Japan 4 6.5% 

India 3 4.8% 

Jordan 2 3.2% 

Iran 1 1.6% 

Austria 1 1.6% 

Italy 2 3.2% 

Spain 1 1.6% 

Switzerland 1 1.6% 

Belgium 1 1.6% 

Germany 1 1.6% 

Albania 1 1.6% 

Ghana 1 1.6% 

South Africa 2 3.2% 

Uganda 1 1.6% 

Botswana 1 1.6% 

Mozambique 1 1.6% 

Nigeria 2 3.2% 

The mix of types of organizations represented is shown in Figure 1. The two largest 

types of organization were federal government at 48% of respondents and university at 

32%. 



32% 

Figure 1 Types of Organizations Represented 

5% 3% 

6% 

6% 

48% 

International 

Federal govt 

State/prov govt 

University 

Private sec<strong>to</strong>r 

Not-for-profit 

Regarding the role in the organization, 76% indicated ‘Scientist’ and 15% selected 

‘Manager’. Some indicated more than one role. The 13% who selected ‘Other’ were: 

• PhD student, 

• Direc<strong>to</strong>r, 

• Analyst, 

• Project Manager, 

• Post doc<strong>to</strong>ral fellow, 

• Lecturer, 

• Marketing and Sales, 

• Project Management and Product R&D and 

• Professor in Remote Sensing. 



4. Current and Planned Use of <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Data 

This section documents the responses <strong>to</strong> Section A of the survey: your current and 

planned use of <strong>Agricultural</strong> Moni<strong>to</strong>ring Data. 

4.1 Current Needs for <strong>Agricultural</strong> Information 

This section describes the responses <strong>to</strong> Question 1: “What are your current needs for 

agricultural information?” The numbers of responses for each application were 

calculated and sorted, and the <strong>to</strong>p twelve applications are shown in Figure 2. 

Figure 2 Major Applications of <strong>Agricultural</strong> Moni<strong>to</strong>ring Data 

50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

0 


Crop Area 

Land Cover 

Crop Yield 

Land Use 

Drought Moni<strong>to</strong>ring 

Soil Moisture 

Crop Health 


Soil Type 



The mix between Research and Operational responses for each of the moni<strong>to</strong>ring 

applications is shown in Table 2. 



Table 2 Research/Operational Mix for Each Moni<strong>to</strong>ring Application 

Crop Production 

Research Operational 


Crop Identification 54% 46% 

Crop Area 51% 49% 

Crop Health 63% 38% 

Crop Yield 64% 36% 

Other 

Water Availability 

58% 42% 

Soil Moisture 73% 27% 

Irrigation 56% 44% 

Flood Moni<strong>to</strong>ring 50% 50% 

Soil Trafficability 62% 38% 

Drought Moni<strong>to</strong>ring 60% 40% 

Other 

Land Management 

33% 67% 

Land Cover 54% 46% 

Land Use 53% 47% 

Soil Type 65% 35% 

Tillage Moni<strong>to</strong>ring 70% 30% 

Crop Residue 80% 20% 

Other 

Ecosystem Health 

80% 20% 

Habitat Type 74% 26% 

Habitat Diversity 80% 20% 

Habitat Connectivity 75% 25% 

Water Erosion 68% 32% 

Wind Erosion 59% 41% 

Salinization 73% 27% 

Desertification 52% 48% 

Other 67% 33% 

The spatial scales of the Crop Production applications are shown in Figure 3 and the 

temporal scales are shown in Figure 4. Other Crop Production-related subject areas 

included: 

• Leaf area index, pigment content 

• Forecasting health 

• Biomass 

• Rangeland 

• Damage assessment 



• Crop development. 

Percent 

Percent 

- 

- 

50 

45 

40 

35 

30 

25 

20 

15 

10 

5 

40 

35 

30 

25 

20 

15 

10 

5 

Figure 3 Spatial Scales of Crop Production Applications 

Int'l 

National 

State/Prov 

Regional 

Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring – Annex 1 7 

Local 

Crop Identification Crop Area Crop Health Crop Yield 

Figure 4 Temporal Scales of Crop Production Applications 

Field 

Daily Weekly Monthly Seasonally Annually Decadal 

Crop Identification Crop Area Crop Health Crop Yield



The spatial scales of the Water Availability applications are shown in Figure 5 and the 

temporal scales are shown in Figure 6. Other Water Availability-related subject areas 

were Soil Survey and Drought Forecasting. 

Percent 

- 

70 

60 

50 

40 

30 

20 

10 

Figure 5 Spatial Scales of Water Availability Applications 

Int'l 


State/Prov 



Local 

Field 

Soil Moisture Irrigation Flood Moni<strong>to</strong>ring 

Soil Trafficability Drought Moni<strong>to</strong>ring

Percent 

- 

40 

35 

30 

25 

20 

15 

10 


5 

Figure 6 Temporal Scales of Water Availability Applications 

Daily 

Weekly 

Monthly 

Seasonally 

Annually 

Decadal 

Soil Moisture Irrigation Flood Moni<strong>to</strong>ring 

Soil Trafficability Drought Moni<strong>to</strong>ring 


The spatial scales of the Land Management applications are shown in Figure 7 and the 

temporal scales are shown in Figure 8. Other Land Management-related subject areas 

were: 

• Soil Survey 

• Rangeland 

• “Good agricultural and environmental conditions” (GAEC) 

• Land degradation 

• Change Detection. 


Percent 

Percent 

40 

35 

30 

25 

20 

15 

10 

5 

- 

- 


Figure 7 Spatial Scales of Land Management Applications 

Int'l 


State/Prov 


Local 

Land Cover Land Use Soil Type 

Tillage Moni<strong>to</strong>ring Crop Residue 

45 

40 

35 

30 

25 

20 

15 

10 

5 

Figure 8 Temporal Scales of Land Management Applications 

Daily 

Weekly 



Annually 

Land Cover Land Use Soil Type 

Tillage Moni<strong>to</strong>ring Crop Residue 


Field 

Decadal



The spatial scales of the Ecosystem Health applications are shown in Figure 9 and the 

temporal scales are shown in Figure 10. Other Ecosystem Health applications were 

Suitability Index and Woody Biomass Potential. 

Percent 

Percent 

- 

60 

50 

40 

30 

20 

10 

45 

40 

35 

30 

25 

20 

15 

10 

5 

- 

Figure 9 Spatial Scales of Ecosystem Health Applications 

Int'l 


State/Prov 



Local 

Habitat Type Habitat Diversity Habitat Connectivity 

Water Erosion Wind Erosion Salinization 

Desertification 

Figure 10 Temporal Scales of Ecosystem Health Applications 

Daily 

Weekly 



Annually 

Field 

Decadal 

Habitat Type Habitat Diversity Habitat Connectivity Water Erosion 

Wind Erosion Salinization Desertification


Other information provided by respondents <strong>to</strong> help understand their needs for agricultural 

moni<strong>to</strong>ring included: 

• “<strong>SAR</strong> Derived DEM for whole nation.” 

• “<strong>SAR</strong> images are absolutely required for crop growth moni<strong>to</strong>ring due <strong>to</strong> cloud 

cover problem in the tropical area.” 

• “Information <strong>to</strong> support climate change, carbon account, deforestation etc. 

especially in the <strong>Agricultural</strong> / Forest fringe regions.” 

• “RADAR may hold potential for us in quantifying flooded areas.” 

• “Our group has used optical remote sensing for over 30 years, and our staff has a 

bias for optics. Radar has always been a mystery <strong>to</strong> us and there is a great need 

for training and simplified products.” 

• “Note <strong>to</strong> clarify selection of time scales: We are a research organization. We 

work in semiarid lands where preciptiation is important and infrequent. So, our 

research is often focused on ‘events’. We often need daily information <strong>to</strong> capture 

the event. For example, for water and wind erosion, annual information would be 

great, but daily information is also required.” 

4.2 Current and Planned Use of <strong>SAR</strong> Data 

The current and planned use of <strong>SAR</strong> data for each application is shown in Table 3. Each 

cell of the table shows the number of respondents who selected this cell. The relative 

values of each cell are more important than the absolute numbers. 

In the Crop Production category, the ‘other’ applications were Rangeland, Crop 

Condition and Crop Development. In ‘Water Availability’, no ‘other’ applications were 

specified. In the Land Management category, ‘Rangeland’ was specified as the only 

additional application. In ‘Ecosystem Health’, ‘Woody biomass potential’, ‘Coastal’ and 

‘Teaching Geology’ were given as additional applications. 

The ‘other’ sensors listed were: 

• Intermap IF<strong>SAR</strong>, 

• ‘Would like <strong>to</strong> use Terra<strong>SAR</strong>-X if available’, and 

• ERS. 


Moni<strong>to</strong>ring 

Application 


Currently Use 

RADARSAT-1 

Plan <strong>to</strong> Use 

Table 3 Current and Planned Use of <strong>SAR</strong> Data 

Currently Use 

RADARSAT-2 


Currently Use 

Envisat A<strong>SAR</strong> 


Currently Use 

Terra<strong>SAR</strong>-X 


Crop Identifica 13 7 16 16 13 7 7 8 10 11 0 8 6 12 3 3 

Crop Area 12 10 14 19 13 6 6 12 11 8 1 6 6 10 1 3 

Crop Health 4 4 5 11 3 3 3 5 5 5 0 2 4 6 0 3 

Crop Yield 4 5 5 17 7 6 4 6 6 7 0 4 5 7 0 1 

Other 1 1 4 3 2 2 0 2 1 3 0 1 0 1 1 1 


Soil Moisture 9 7 12 16 8 6 5 6 12 8 0 6 6 11 1 2 

Irrigation 1 4 1 6 1 2 2 3 1 2 0 3 1 3 0 0 

Flood Moni<strong>to</strong>r 8 7 9 15 5 6 3 7 6 5 1 7 5 8 0 2 

Soil Trafficabi 2 1 2 2 1 1 1 1 0 1 0 1 0 2 0 0 

Drought Monit 2 4 4 10 3 4 2 3 2 5 0 2 2 6 0 0 

Other 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 


Land Cover 6 7 6 15 5 8 3 6 6 6 0 5 4 8 0 2 

Land Use 4 6 4 12 1 6 3 5 4 4 1 4 2 4 0 0 

Soil Type 2 5 3 9 0 3 1 4 1 3 0 3 1 4 1 0 

Tillage Moni<strong>to</strong> 3 5 6 8 3 4 4 8 3 5 0 6 5 8 0 0 

Crop Residue 1 5 2 6 1 3 2 6 0 3 0 4 3 4 1 0 

Other 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 


Habitat Type 1 4 3 4 0 3 1 3 0 1 0 1 1 4 0 0 

Habitat Divers 1 3 3 4 0 2 1 3 0 1 0 1 2 4 0 0 

Habitat Conne 1 3 2 3 0 2 1 3 0 1 0 1 1 4 0 0 

Water Erosion 1 3 2 5 0 3 1 3 0 2 0 2 1 4 0 0 

Wind Erosion 1 3 1 3 0 2 1 2 0 2 0 2 1 4 0 0 

Salinization 1 3 3 7 0 3 1 3 1 4 0 3 2 4 0 0 

Desertification 1 4 1 6 0 4 1 6 0 4 0 4 2 5 0 0 

Other 1 1 1 3 0 0 0 1 2 0 0 2 2 0 1 0 


Currently Use 

ALOS PAL<strong>SAR</strong> 



Currently Use 

Cosmo-Skymed 


RISAT 


Sentinel-1 


Currently Use 

Other 

Plan <strong>to</strong> Use


The answers <strong>to</strong> the question ‘Approximately how many scenes of <strong>SAR</strong> do you currently 

use in one year?’ are shown in Figure 11. 

18 

16 

14 

12 

10 

8 

6 

4 

2 

0 

Figure 11 Approximate Number of <strong>SAR</strong> Scenes Used per Year 

500 

4.3 Current and Planned Use of Optical Data 

The current and planned use of optical data is shown in Table 4. Again, each cell of the 

table shows the number of respondents who selected this cell and the relative values of 

each cell are more important than the absolute numbers. 





Currently Use 

AVHRR 

Table 4 Current and Planned Use of Optical Data 


Currently Use 

MODIS 


Currently Use 

MERIS 


Currently Use 

AWiFS 


Currently Use 

Landsat 


In the Crop Production category, the ‘other’ applications were Rangeland, Background 

Information and Crop Development. In ‘Water Availability’, ‘snow cover’ was specified 

in other applications. In the Land Management category, ‘Rangeland’ and ‘Change 

Detection’ were specified as additional applications. In ‘Ecosystem Health’, ‘Woody 

biomass potential’, ‘Coastal’ and ‘Geology’ were given as additional applications. 

The ‘other’ sensors listed were the following. The frequency of each response is given in 

parentheses. 

• ALOS/AVNIR-2 (2), 

Currently Use 


Currently Use 


Currently Use 


Crop Identification 6 4 8 7 0 4 11 8 17 11 12 13 5 4 4 4 3 9 7 7 7 7 6 2 

Crop Area 7 4 12 8 3 2 10 7 19 7 11 12 5 4 4 4 2 8 8 7 6 7 5 3 

Crop Health 9 6 15 10 0 3 6 6 6 2 6 5 2 1 2 2 1 4 2 4 1 4 4 0 

Crop Yield 8 6 15 10 1 3 4 7 9 5 7 6 1 2 1 2 1 5 3 3 1 3 3 0 

Other 1 0 1 1 0 2 0 2 3 0 1 1 0 1 0 1 1 1 0 1 0 1 1 0 


Soil Moisture 4 2 7 4 0 3 1 2 3 3 2 3 0 1 1 2 1 2 0 2 0 2 2 0 

Irrigation 2 2 3 3 0 1 4 2 2 2 0 3 0 1 0 2 0 1 0 1 0 1 1 0 

Flood Moni<strong>to</strong>ring 5 3 11 6 0 2 3 2 9 5 4 4 1 2 2 3 0 2 1 2 1 2 2 0 

Soil Trafficability 2 1 3 1 0 2 0 1 1 0 0 1 0 1 0 2 0 1 0 1 0 1 0 0 

Drought Moni<strong>to</strong>ring 12 6 19 11 1 3 2 4 4 3 5 4 0 1 1 2 1 1 1 2 1 2 1 0 

Other 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 


Land Cover 7 2 13 8 4 3 6 8 24 10 9 9 2 2 3 5 2 7 7 7 4 6 3 1 

Land Use 5 1 8 6 3 2 3 6 19 9 10 8 3 3 4 6 2 5 6 8 3 7 3 0 

Soil Type 3 1 4 5 1 2 0 2 7 3 1 3 0 1 0 2 0 2 2 5 1 4 2 0 

Tillage Moni<strong>to</strong>ring 2 1 1 2 0 2 0 4 2 1 1 3 0 2 0 2 0 2 0 2 0 2 0 0 

Crop Residue 2 1 1 4 0 1 1 5 4 2 2 3 2 2 0 2 0 2 0 2 0 2 0 0 

Other 0 0 1 1 0 1 1 1 2 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 


Habitat Type 3 1 3 4 0 2 1 3 7 3 2 5 1 1 0 2 1 3 3 5 2 4 1 0 

Habitat Diversity 2 1 2 4 0 2 0 3 5 3 2 4 1 1 0 2 1 3 2 5 2 4 2 0 

Habitat Connectivity 2 1 2 4 0 2 1 3 4 2 2 4 0 1 0 2 0 2 2 4 2 4 1 0 

Water Erosion 2 1 3 2 0 2 0 2 4 2 0 2 1 1 0 2 0 1 0 1 0 1 0 0 

Wind Erosion 2 1 2 2 0 2 0 2 1 1 0 2 0 1 0 2 0 1 0 1 0 1 0 0 

Salinization 1 1 2 1 0 2 0 3 1 0 1 1 1 1 0 2 0 2 0 1 0 1 0 0 

Desertification 6 3 7 5 1 2 0 2 6 3 2 4 1 1 0 2 0 2 1 3 1 3 0 1 

Other 1 0 2 0 0 1 0 1 3 1 0 1 0 1 0 1 0 1 0 2 0 2 0 0 

SPOT-4/5 


LISS 

CEBERS 


Currently Use 

Rapid-Eye 


Currently Use 

IKONOS 


Currently Use 

Quickbird 


Other 

Currently Use 



• ASTER (3), 

• GeoEye-1 (2), 

• Eros B (1), 

• Airborne systems (1), 

• Feng Yun 1D (CHRPT) (1), 

• hyperspectral data (1), 

• LiDAR (1), 

• GreenSeeker (1) and 

• Kompsat (1). 

4.4 Current and Planned Use of In-situ Data 

The current and planned use of in-situ data is shown in Table 5. The column on the right 

side contains a sample of the types of in-situ data. In each application, many ‘current’ 

responses were repeated in ‘planned’, but <strong>to</strong> keep the table reasonably brief, the ‘current’ 

responses were not necessarily repeated in the ‘planned’ row. The numbers in 

parentheses indicate the frequency of each response. 



Crop 

Production 

Crop 

Identification 

Crop Area 

Table 5 Current and Planned Use of In-situ Data 

Current / 

Planned 

# 


Sample of Types of In-situ Data 

Current 37 Field surveys (28), spectroradiometer (3), 

meteorological data (4), crop type (2), GIS data 

(1), crop calendar (1), producer records (1), 

National <strong>Agricultural</strong> Statistics Service (1), 

video & pho<strong>to</strong> (1), administrative data (1), road 

side survey (1) 

Planned 17 Crop insurance records (1), permanent training 

sites (1), DEM (1) 

Current 35 Field survey (26), statistical data (4), 

meteorological data (3), crop type survey (2), 

spectroradiometer (1), GIS data (1), his<strong>to</strong>rical 

crop statistics (1), current crop calendar (1), 

producer records (1), Corine Land Cover (1), 

<strong>to</strong>tal area (1), administrative data (1), video & 

pho<strong>to</strong> (1) 

Planned 18 Spectropho<strong>to</strong>metric data (1), Real-time 

information from fields (1), his<strong>to</strong>rical maps (1) 

Crop Health Current 20 Field surveys (14), meteorological data (7), 

spectroradiometer (2), GPS (1), water salinity 

measurements (1), crop growth parameters 

(1), Leaf Area Index (LAI) (1), biomass (1), 

SPAD (chlorophyll) (1), crop growth variables 

(1), crop type (1), management practices (1) 




Crop Yield 

Other: 

Rangeland, 

Crop moni<strong>to</strong>ring, 

Crop 

development 

Water 

Availability 

Soil Moisture 


Flood 


Soil Trafficability 

Drought 



Other: snow 

cover Planned 1 

Current / # 


Planned Responses 

Planned 13 Permanent validation sites (1), 

Spectropho<strong>to</strong>metric data (1) 

Current 27 Field surveys (14), meteorological data (9), 

his<strong>to</strong>rical crop statistics (4), statistical yield 

survey data (3), LAI (3), production data (3), 

soil properties (2), biomass (2), plant height 

(1), numbers of plants per metre (1), variety 

(1), declivity (1), SPAD (chlorophyll) (1) 

Planned 18 Crop biomass - <strong>SAR</strong> relationship (1), yield gap 

potentials (1), varietal behavior (1) 

Current 3 Field surveys (3), confidential databases 

supplied by collabora<strong>to</strong>rs (1) , LAI 

measurements (1) 

Planned 1 Meteorological data (1) 

Current 29 Meteorological data (14), field surveys (13), 

soil moisture data from moisture probes (3), 

soil type and properties (2), theta probe 

meaurements (2), soil samples (2), gravimetric 

measurement (1), climate data (1), TDRs (1), 

mini-trase (1), roughness (1) 

Planned 18 Sensor network (2), mesonet (1), DEM (1), 

slope and runoff models (1) 

Current 7 Field surveys (2), gravimetric measurement 

(1), soil type and properties (1), climate data 

(1), inputs from farmers (1), statistical 

agriculture information (1), meteorological data 

(1), administrative data (1) 

Planned 5 Sensor network (1) 


gauges and sensors (1), extensive permanent 

moni<strong>to</strong>ring instrumentation (1), intensive field 

experiments (1), hydrological models (1), GPS 

surveys (1), administrative data (1) 

Planned 5 In situ sensor network (1) 

Current 4 Meteorological data (3) 

Planned 3 Field surveys (1) 


intensive field experiments (2), NRT (near realtime) 

climatic data (1), inputs from farmers (1), 

US Drought Moni<strong>to</strong>r (1), extensive permanent 

moni<strong>to</strong>ring instrumentation (1), biomass 

sampling (1) 

Planned 13 Crop status (2), soil moisture (2) 

Current 1 Meteorological data (1) 




Land 

Management 

Land Cover 

Land Use 

Soil Type 

Tillage 



Current / 

Planned 

# 




GPS surveys (2), aerial surveys (2), ancillary 

maps (1), crop calendar (1), census data (1), 

Corine Land Cover (1), GIS information (1), 

administrative data (1) 

Planned 12 National Forest Inven<strong>to</strong>ry (1), remote sensing 

derived land maps (1) 

Current 24 Field surveys (18), GPS (2), Soil surface 

roughness (1), meteorological data (1), 

demographic (1), aerial surveys (1), census 

data (1), GIS information (1), administrative 

data (1) 

Planned 12 National Forest Inven<strong>to</strong>ry (1) 

Current 10 Field surveys (4), soil profiles (2), agricultural 

census data (1), meteorological data (1) 

Planned 7 Sensor network (1) 


surface roughness instruments (pin board) (1), 

needle profiler measurements (1) 

Planned 6 Laser profiler measurements (2) 

Crop Residue Current 8 Field surveys (5), vertical pho<strong>to</strong>graphs (2), 

inputs from farmers (1), meteorological data 

(1), crop type (1) 

Planned 4 

Other: 

Current 1 Field surveys (1) 

rangeland 

Ecosystem 

Health 

Planned 0 

Habitat Type Current 7 field surveys (6), meteorological data (1) 

Planned 4 

Habitat Diversity Current 5 Field surveys (4) 

Planned 3 Meteorological data (1) 

Habitat 

Current 4 Field surveys (3) 

Connectivity Planned 3 Meteorological data (1) 

Water Erosion Current 7 Field surveys (2), meteorological data (2), soil 

data and models (1), extensive permanent 


experiments (1), GPS surveys (1), census data 

(1) 

Planned 3 

Wind Erosion Current 4 Field surveys (1), meteorological data (1), soil 

data and models (1), extensive permanent 


experiments (1) 

Planned 3 




Salinization 


Other:vegetation 

biomass, woody 

biomass 

potential, 

coastal 


Current / # 


Planned Responses 

Current 3 Field surveys (2), meteorological (1), water 

salinity measurements (1) 

Planned 4 

Current 10 Field surveys (6), meteorological (2), water 

salinity measurements (1), extensive 

permanent moni<strong>to</strong>ring instrumentation (1), 

intensive field experiments (1), GPS surveys 

(1), census data (1) 

Planned 4 

Current 4 Field surveys (2), meteorological (1), GPS 

surveys (1), census data (1) 

Planned 2 

4.5 Information Requirements and Application Status 

A sample of the information requirements for specific moni<strong>to</strong>ring applications are shown 

in Table 6. These are the answers <strong>to</strong> Question 5a. The information requirements of a 

number of responses have been reported in each cell. The possible range of responses are 

shown with the frequency (and %) in each range. The ‘Other’ column shows ‘Other 

Information Requirements’ contributed by the respondents. 

Table 6 Information Requirements for Specific Moni<strong>to</strong>ring Applications 

Application Product 

Accuracy 

Crop Area 60-70% : 

70-80% : 2 (17%) 

80-90% : 6 (50%) 

>90% : 4 (34%) 

Crop Condition 60-70% : 

70-80% : 

80-90% : 

>90% : 

Spatial 

resolution/ 

mapping unit 

500 m : 

500 m : 1 

(50%) 

Frequency of 

delivery/ 

reporting 

Daily: 

Weekly: 2 (12%) 

Monthly: 7 (41%) 

Seasonally: 6 

(35%) 

Yearly: 2 (12%) 

Decadal: 

Daily: 

Weekly: 2 (100%) 

Monthly: 

Seasonally: 

Yearly: 

Decadal: 

Timeliness of 

delivery/ 

reporting 

1 Day: 3 (21%) 

1 Week: 5 (36%) 

1 Month: 2 (14%) 

1 Month after 

harvest: 1 (7%) 

1 Month before 


1 Season: 1 (7%) 

1 Year: 1 (7%) 

1 Day: 1 (100%) 

1 Week: 

1 Month: 

1 Season: 

1 Year: 

Other 

Sampling 

method; 

mainly rice; 



Accuracy 

Crop Health 60-70% : 

70-80% : 

80-90% : 1 (33%) 

>90% : 2 (67%) 

Crop 


Crop 

Production/Yield 


60-70% : 

70-80% : 1 (17%) 

80-90% : 2 (33%) 

>90% : 3 (50%) 

60-70% : 

70-80% : 2 (14%) 

80-90% : 9 (64%) 

>90% : 3 (21%) 

Crop Residue 60-70% : 

70-80% : 

80-90% : 1 

(100%) 

>90% : 

Crop Stress 60-70% : 

70-80% : 1 

(100%) 

80-90% : 

>90% : 

Desertification 60-70% : 

70-80% : 

80-90% : 1 

(100%) 

>90% : 

Drought 

moni<strong>to</strong>ring 

60-70% : 

70-80% : 

80-90% : 

>90% : 

Spatial 


mapping unit 

500 m : 

500 m : 1 

(14%) 

500 m : 1 

(6%) 

500 m : 

500 m : 

500 m : 

500 m : 1 

(100%) 

Frequency of 

delivery/ 

reporting 

Daily: 

Weekly: 2 (67%) 



Yearly: 

Decadal: 

Daily: 

Weekly: 2 (25%) 



(25%) 

Yearly: 3 (38%) 

Decadal: 

Daily: 

Weekly: 6 (32%) 



(32%) 

Yearly: 2 (11%) 

Decadal: 

Daily: 

Weekly: 1 (100%) 



Yearly: 

Decadal: 

Daily: 

Weekly: 1 (25%) 



Yearly: 3 (75%) 

Decadal: 

Daily: 

Weekly: 



Yearly: 1 (100%) 

Decadal: 

Daily: 

Weekly: 1 (100%) 



Yearly: 

Decadal: 

Timeliness of 

delivery/ 

reporting 

1 Day: 1 (25%) 

1 Week: 2 (50%) 



1 Year: 

1 Day: 3 (38%) 

1 Week: 

1 Month (after 

harvest): 5 (63%) 


1 Year: 

1 Day: 1 (6%) 

1 Week: 7 (44%) 


1 Month after 


1 Month before 



1 Year: 

1 Day: 

1 Week: 



1 Year: 

1 Day: 

1 Week: 1 (100%) 


harvest): 


1 Year: 

1 Day: 

1 Week: 

1 Month: 1 

(100%) 


1 Year: 

1 Day: 

1 Week: 


harvest): 


1 Year: 

Other 

Integrated 

geospatial 

datasets 

Mainly rice; 

Precision 

geocoded and 

calibrated data 

Sampling 

method; Yield 

map, or yield 

data at field 

scale; Full 

polarization 



Accuracy 

Ecosystem 

health 

Flood 


Land Cover/ 

Land Use 

Land 

Management 


60-70% : 

70-80% : 

80-90% : 1 

(100%) 

>90% : 

60-70% : 

70-80% : 

80-90% : 

>90% : 1 (100%) 

60-70% : 

70-80% : 

80-90% : 5 

(100%) 

>90% : 

60-70% : 

70-80% : 

80-90% : 2 

(100%) 

>90% : 

Land Use 60-70% : 

70-80% : 

80-90% : 2 

(100%) 

>90% : 

Rangeland 

health 

Rangeland 

mapping 

60-70% : 

70-80% : 

80-90% : 1 

(100%) 

>90% : 

60-70% : 

70-80% : 

80-90% : 1 

(100%) 

>90% : 

Spatial 


mapping unit 

500 m : 

500 m : 

500 m : 

500 m : 1 

(25%) 

500 m : 

500 m : 

500 m : 

Frequency of 

delivery/ 

reporting 

Daily: 

Weekly: 



(100%) 

Yearly: 

Decadal: 

Daily: 1 (33%) 

Weekly: 



(33%) 

Yearly: 1 (33%) 

Decadal: 

Daily: 

Weekly: 



(50%) 

Yearly: 1 (25%) 

Decadal: 

Daily: 

Weekly: 



(50%) 

Yearly: 2 (50%) 

Decadal: 

Daily: 

Weekly: 



Yearly: 4 (100%) 

Decadal: 

Daily: 

Weekly: 



Yearly: 1 (100%) 

Decadal: 

Daily: 

Weekly: 



Yearly: 1 (100%) 

Decadal: 

Timeliness of 

delivery/ 

reporting 

1 Day: 

1 Week: 


harvest): 


1 Year: 

1 Day: 1 (100%) 

1 Week: 


harvest): 


1 Year: 

1 Day: 

1 Week: 


harvest): 1 (33%) 


1 Year: 2 (67%) 

1 Day: 

1 Week: 


harvest): 


1 Year: 2 (67%) 

1 Day: 

1 Week: 



1 Year: 

1 Day: 

1 Week: 

1 Month : 

1 Season: 1 

(100%) 

1 Year: 

1 Day: 

1 Week: 



(100%) 

1 Year: 

Other 



Accuracy 

Rice moni<strong>to</strong>ring 60-70% : 

70-80% : 

80-90% : 

>90% : 2 (100%) 

Soil Moisture +/- 10-15% : 4 

(33%) 

+/- 5-10% : 2 

(17%) 

+/- 90% : 

Soil trafficability 60-70% : 

70-80% : 1 

(100%) 

80-90% : 

>90% : 

Tillage 


Water 

Availability 


60-70% : 

70-80% : 1 (50%) 

80-90% : 1 (50%) 

>90% : 

60-70% : 

70-80% : 

80-90% : 1 (50%) 

>90% : 1 (50%) 

Spatial 


mapping unit 

500 m : 

500 m : 3 

(23%) 

500 m : 

500 m : 

500 m : 

500 m : 1 

(33%) 

Frequency of 

delivery/ 

reporting 

Daily: 

Weekly: 



(100%) 

Yearly: 

Decadal: 

Daily: 2 (17%) 

Weekly: 10 (83%) 



Yearly: 

Decadal: 

Daily: 

Weekly: 



Yearly: 

Decadal: 

Daily: 1 (100%) 

Weekly: 



Yearly: 

Decadal: 

Daily: 

Weekly: 2 (67%) 



Yearly: 1 (33%) 

Decadal: 

Daily: 

Weekly: 2 (67%) 



Yearly: 

Decadal: 1 (33%) 

Timeliness of 

delivery/ 

reporting 

1 Day: 

1 Week: 


harvest): 


(100%) 

1 Year: 

1 Day: 8 (73%) 

1 Week: 1 (9%) 



1 Year: 

1 Day: 

1 Week: 


harvest): 


1 Year: 

1 Day: 1 (100%) 

1 Week: 


harvest): 


1 Year: 

1 Day: 

1 Week: 



(100%) 

1 Year: 

1 Day: 

1 Week: 1 (50%) 



1 Year: 1 (50%) 

Other 

Modelled 

inputs/outputs; 

comparison 

with normal 

year; Full 

polarization 



The percentages of moni<strong>to</strong>ring applications which are in the Research stage versus 

Pilot/Demonstration versus Operational are shown in Figure 12. 

Figure 12 Ratio of Applications at Varying Stages 

22% 

22% 

56% 

Research 

Pilot/demo 


The percentages of the various levels of importance of <strong>SAR</strong> data for the agricultural 

moni<strong>to</strong>ring applications are shown in Figure 13. 



Figure 13 Importance of <strong>SAR</strong> Data for <strong>Agricultural</strong> Moni<strong>to</strong>ring Applications 

39% 

8% 

8% 

45% 

Not important 

Somewhat 

Increasingly 

Critical 



5. Views on How <strong>to</strong> Encourage Greater Use of <strong>SAR</strong> Data 

This section documents the responses <strong>to</strong> Section B of the survey: Your views on how <strong>to</strong> 

encourage greater use of <strong>SAR</strong> data. 

5.1 Encouraging Greater Use of <strong>SAR</strong> Data for <strong>Agricultural</strong> 


Question 6 asked: “What fac<strong>to</strong>rs encourage the greater use of <strong>SAR</strong> data for your 

agricultural moni<strong>to</strong>ring needs? (Rate each from 1 <strong>to</strong> 5 with 1 being least important and 5 

being most important.” The replies were multiplied by the weighting fac<strong>to</strong>r (1 – 5) and 

the results were added for each response. The <strong>to</strong>tals were then sorted <strong>to</strong> show highest 

scores first. The result is shown in Table 7. 

Table 7 Fac<strong>to</strong>rs that Encourage Greater Use of <strong>SAR</strong> Data 

Reduced data cost 264 

Improved availability of models or methods <strong>to</strong> derive information 

from data 258 

Improved knowledge <strong>to</strong> use <strong>SAR</strong> data through training 243 

Improved image quality and calibration 240 

Reduced gap between science and operational users 236 

Desired <strong>SAR</strong> frequency (wavelength) available 233 

Greater availability of software <strong>to</strong>ols and applications 231 

Improved timeliness of satellite data access 230 

Easier data discovery and order process 230 

Desired <strong>SAR</strong> polarization available 229 

Greater access <strong>to</strong> new acquisitions 229 

Improved data policies (e.g. data sharing) 229 

Higher spatial (pixel) resolution 224 

Access <strong>to</strong> geometrically corrected or ready-<strong>to</strong>-use data 224 

Policy of commitment <strong>to</strong> data continuity 222 

Greater access <strong>to</strong> application specific training 218 

Greater availability of expert personnel 217 

Improved revisit/re-look of satellite passes 216 

Improved spatial (swath) coverage 211 

Improved data interoperability or integration 204 

Greater access <strong>to</strong> his<strong>to</strong>rical data archives 203 



Many of the fac<strong>to</strong>rs are clustered with similar scores, and the lowest score is quite high. 

It appears that respondents consider all of these fac<strong>to</strong>rs <strong>to</strong> be important. 

Other fac<strong>to</strong>rs that respondents added include the following: 

• “Deeper signal band (25-50 cm).” 

• “Possibility <strong>to</strong> explore S-band <strong>SAR</strong> for agriculture.” 

• “<strong>SAR</strong> satellites should be designed for moni<strong>to</strong>ring, i.e. no mode switching, ideally 

100 % duty cycle, large swath width.” 

• “We have found that measuring parcel areas with VHR <strong>SAR</strong> data was not 

accurate enough for the control of CAP subsidies. As for the check of land cover 

and GAEC, more research is probably needed as we have not yet been fully 

convinced by the use of <strong>SAR</strong> data for such purposes.” 

• “(I am) not in the business of agricultural moni<strong>to</strong>ring but generally speaking there 

is a need for the development of "killer" applications. You may have some in 

agriculture but there doesn't seem <strong>to</strong> be any at this point in time in forestry with 

the currently available technology.” 

• “Operational software for various applications.” 

• “Ability <strong>to</strong> record forward scatter also.” 

• “Availability of the necessary technical training and application projects for 

continuity of acquired skills.” 

• “I also consider very important the availability of announcements of opportunities 

<strong>to</strong> obtain RADAR data for research purposes.” 

5.2 Encouraging Greater Use of <strong>SAR</strong> Data through Science 

Question 7 asked: “What suggestions do you have <strong>to</strong> encourage greater use of <strong>SAR</strong> data 

through science? (Rate each from 1 <strong>to</strong> 5 with 1 being least important and 5 being most 

important.” Again, the replies were multiplied by the weighting fac<strong>to</strong>r (1 – 5) and the 

results were added for each response. The <strong>to</strong>tals were then sorted <strong>to</strong> show highest scores 

first. The result is shown in Table 8. Again, the scores do not show a wide range, 

indicating that while there is a ranking in importance, all of the fac<strong>to</strong>rs were considered 

important. 


• “Selection of the best <strong>SAR</strong> frequency for each commodity (i.e S-band for rice).” 

• “Science must come up with accurate, reliable and transferable geophysical 

products.” 

• “<strong>SAR</strong> is a complex data product. There needs <strong>to</strong> be development of simple 

processes <strong>to</strong> extract information so that it is readily available, reliable, and 

understandable <strong>to</strong> users. Visibility of the operational use of these products needs 

<strong>to</strong> be marketed.” 

• “Assured regular acquisition of <strong>SAR</strong> data.” 

• “Greater availability of assured data and software.” 



• “A community of practice with regular exchange meetings is very important for 

knowledge sharing among key players in the practice.” 

Table 8 Fac<strong>to</strong>rs that Encourage Greater Use of <strong>SAR</strong> Data Through Science 

Encourage data providers <strong>to</strong> provide access <strong>to</strong> new and 

archived data for research projects, at no or minimal cost 274 

Establish demonstration projects around agricultural themes 

which include both the science and user communities 254 

Create a <strong>SAR</strong> training manual for agriculture 251 

Establish a website portal which potentially could include 

training on <strong>SAR</strong> methods and best practice for agriculture 

applications, glossary of terms, list of experts, user forums 

and links <strong>to</strong> <strong>SAR</strong> data providers and other resources 250 

Develop an agricultural <strong>SAR</strong> data community of practice 245 

Through the GEO web site, provide links <strong>to</strong> national and 

international announcements of opportunities and other 

funding opportunities 245 

Enhance scientific exchanges through annual/bi-annual 

agriculture Workshops 243 

Establish international super sites <strong>to</strong> facilitate research 

collaborations 233 

Use of online <strong>to</strong>ols <strong>to</strong> disseminate information 233 

Develop an inven<strong>to</strong>ry of international science activities and 

<strong>SAR</strong> experts 231 

Target special journal and book publications <strong>to</strong> disseminate 

state of the art science, specific for agriculture 210 

5.3 Encouraging Greater Operational Use of <strong>SAR</strong> Data 

Question 8 asked: “What suggestions do you have <strong>to</strong> encourage greater operational use of 

<strong>SAR</strong> data? (Rate each from 1 <strong>to</strong> 5 with 1 being least important and 5 being most 

important.” The weighted scores were calculated and sorted as described above. The 

result is shown in Table 9. 


• “UAV-<strong>SAR</strong> should be available in commercial market <strong>to</strong> widen the access <strong>to</strong> 

airborne <strong>SAR</strong> data.” 

• “Suitable data and software may be made available.” 

• “Ready <strong>to</strong> use and feed forward data in standard formats.” 



• “The last point is the most important! 1 ” 

Table 9 Fac<strong>to</strong>rs that Encourage Greater Operational Use of <strong>SAR</strong> Data 

Influence existing and new <strong>SAR</strong> sensor development by 

documenting the requirements of the agriculture community 

with respect <strong>to</strong>: 

• Spatial resolution 

• Spatial coverage 

• Temporal revisit/re-look 

• Data latency/delivery 

• <strong>SAR</strong> sensor parameters (frequency, polarizations, angles) 265 

Document best practices or approaches <strong>to</strong> designing area 

sample frameworks, data pre-processing and processing, 

methods and accuracy assessments related <strong>to</strong> <strong>SAR</strong> and 

agriculture 251 

Encourage international space agencies <strong>to</strong> consider data 

exchanges <strong>to</strong> meet the needs of the agriculture community 246 

Develop a consensus on data policy needs for agriculture and 

design a strategy <strong>to</strong> negotiate liberal data policies 239 

Coordinate data procurement <strong>to</strong> take advantage of economies 

of scale 221 

Create an international team <strong>to</strong> assess interoperability among 

sensors and <strong>to</strong> design approaches <strong>to</strong> inter-calibrate among 

sensors 220 

Encourage the creation of <strong>SAR</strong> satellite constellations <strong>to</strong> 

improve data continuity, revisit, provide consistency and 

prevent redundancy 133 

1 However, the last point scored lowest! Some of the surveys that were sent out unfortunately did not allow 

the respondents <strong>to</strong> enter a score for the last row, and this may explain the low score. 



5.4 Encouraging Greater Capacity <strong>to</strong> Use <strong>SAR</strong> Data 

Question 9 asked: “What suggestions do you have <strong>to</strong> encourage greater capacity <strong>to</strong> use 

<strong>SAR</strong> data? (Rate each from 1 <strong>to</strong> 5 with 1 being least important and 5 being most 

important.” The weighted scores were calculated and sorted as described above. The 

result is shown in Table 10. 

Table 10 Fac<strong>to</strong>rs that Encourage Greater Capacity <strong>to</strong> Use <strong>SAR</strong> Data 

Encourage <strong>SAR</strong> software developers <strong>to</strong> provide software for 

educational purposes at minimal or no cost 261 

Encourage <strong>SAR</strong> data providers <strong>to</strong> provide sample data for educational 

purposes so that new users can practice what they have learned 257 

Through the GEO <strong>Agricultural</strong> Moni<strong>to</strong>ring Community of Practice, build 

a network of networks in <strong>SAR</strong> and agriculture <strong>to</strong> coordinate/share 

resources and experiences 252 

Develop, disseminate and update <strong>SAR</strong> training courses and technical 

manuals specifically designed for the agriculture community 251 

Develop lecture series on <strong>SAR</strong> and agriculture which could be 

included in university and college courses 249 

Through the GEO <strong>Agricultural</strong> Moni<strong>to</strong>ring Community of Practice, 

compile list of potential avenues for obtaining funds for infrastructure 

development (computers, software, Internet, etc). <strong>to</strong> facilitate greater 

use of EO data and capacity building opportunities. 235 

Through the international GEO website, advertise opportunities for 

internships and exchanges with leading <strong>SAR</strong> institutions 232 

Through the GEO <strong>Agricultural</strong> Moni<strong>to</strong>ring Community of Practice, 

develop user forums <strong>to</strong> drive capacity needs 223 


• “Reduced data cost.” 

• “Low cost <strong>SAR</strong> sensor (data) should be made commercially available in the 

market, as easily as the existing COTS optical sensor (data).” 

• “We certainly need <strong>to</strong> be trained on the operational use of <strong>SAR</strong> data for 

agriculture and large scale land cover mapping in general.” 

• “Demonstrate operational use of <strong>SAR</strong> data for crop assessment and moni<strong>to</strong>ring.” 

• “Show case operational ability of <strong>SAR</strong> and availability of a few (images of) free 

data <strong>to</strong> new users.” 

• “GEO can establish an International digital reposi<strong>to</strong>ry and enable CMS for 

updates on emerging technologies.” 



5.5 Other Suggestions <strong>to</strong> Encourage Greater Use of <strong>SAR</strong> Data 

In answer <strong>to</strong> Question 10, other suggestions <strong>to</strong> encourage greater use of <strong>SAR</strong> data 

include: 

• “Encourage Research & Development with additional funding <strong>to</strong> University and 

Department Research Branch.” 

• “Focus on funding demonstration related projects.” 

• “A price policy should be created <strong>to</strong> differentiate price for retail and bulk image 

procurement. <strong>Agricultural</strong> applications require bulk images, which should be 

given a special discount price compared <strong>to</strong> the retail price. Otherwise, the image 

cost will be the barrier for <strong>SAR</strong> applications in Agriculture.” 

• “Real use of <strong>SAR</strong> data only starts when mature higher-level products become 

readily available. This applies both <strong>to</strong> scientific and commercial applications.” 

• “Link young researchers or users <strong>to</strong> experienced researchers in joint projects for 

men<strong>to</strong>ring, knowledge and skills transfer (particularly North-South joint projects). 

Enable users <strong>to</strong> produce relevant operational products that have multiple uses.” 

• “<strong>SAR</strong> data be sold at an affordable price <strong>to</strong> the users.” 

• “Disseminate relevant case studies.” 

• “Develop application-specific demonstration packages (Procedure and S/W 

package) and provide sample data free of cost for the area of a user concerned.” 

• “Operational use of data relies on defined products that have demonstrated use. 

Products must have known delivery times and known prices.” 

• “To determine the needs of the users and show examples of how radar can be 

used <strong>to</strong> answer those questions. This information also needs <strong>to</strong> be provided <strong>to</strong> 

managers of government departments so they ‘buy’ in<strong>to</strong> EO and radar 

technology.” 

• “Develop training videos at low cost.” 

• “Showing case studies of better accuracy, techniques, data and software would 

encourage greater use of <strong>SAR</strong>.” 

• “The biggest impediment is cost. If data costs were low (free), the community 

would use the data and operational applications would be built.” 

• “Involve the media in focusing on the importance of <strong>SAR</strong> technologies.” 

• “The complexities of the Mathematics and Physics of <strong>SAR</strong> - Target interactions 

should be brought in at a later stage once the user has an understanding of basic 

interactions. Single polarized multi-temporal <strong>SAR</strong> should be the stepping s<strong>to</strong>ne 

followed by dual polarized data. The basics of the <strong>SAR</strong>-agriculture interaction as 

a ready reckoner could be made available for a given frequency and polarization 

at sensor level and geometry and wetness at target level. This may help <strong>to</strong> break 

the ice for users. Secondly, data costs have <strong>to</strong> come down considerably.” 

• “I am working with RADARSAT-2 images thanks <strong>to</strong> the SOAR Program, and 

this is providing me and my research team the opportunity of high technology of 

very high cost data, <strong>to</strong>tally free. More images for researchers and university 

teaching programs are necessary. Remember that our students of <strong>to</strong>day are the 



future users of <strong>SAR</strong> images. Very low or no cost images for research in 

developing countries should be provided, this should be done with more seminars 

or teaching courses in use of <strong>SAR</strong> images in developing countries. I think that the 

GEO <strong>Agricultural</strong> Moni<strong>to</strong>ring Community of Practice is an important step in this 

way.” 

• “Education and tailor made training courses.” 

• “I think the upcoming workshop is a good step but there needs <strong>to</strong> be follow-on.” 

• “Develop a <strong>SAR</strong> constellation that produces continuous systematic observations 

throughout the growing season.” 

• “Strongly consider long term calibration and validation sites for all agricultural 

mapping sensor data in developing countries. Help build functional networks on 

ground data or in-situ moni<strong>to</strong>ring processes. Consider modeling socio-economic 

issues on operational product development using <strong>SAR</strong> data.” 

• “Increased capacity building through training and availability of <strong>SAR</strong> products 

and <strong>to</strong>ols for analysis and interpretation, particularly <strong>to</strong> new users from 

developing regions, will go a long way <strong>to</strong> encouraging greater use of <strong>SAR</strong> for 

<strong>Agricultural</strong> Moni<strong>to</strong>ring.” 

• “Online training is a key component, particularly for advanced <strong>SAR</strong> 

(polarimetry/inferometry) - academia should be included!” 

• “Focus on workshop sessions <strong>to</strong> run examples. Explore different applications; get 

people/potential users engaged and involved in thinking about work they do that 

could use <strong>SAR</strong> information.” 

5.6 Other Views 

In answer <strong>to</strong> Question 11, other views that are relevant <strong>to</strong> the International GEO 

Workshop on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring include: 

• “Develop databank of standardized/representative values for surface roughness 

for different crop or soil conditions.” 

• “Exploration on the possibility of a <strong>SAR</strong> payload in a micro-satellite should be 

discussed, <strong>to</strong> compete with the existing optic payloads operated in most of the 

existing micro-satellites.” 

• “<strong>SAR</strong> systems (sensor + ground segment) should be designed <strong>to</strong> allow continuous 

moni<strong>to</strong>ring, i.e. data flows should be completely au<strong>to</strong>matic. Image ordering, 

manual processing, etc. should become a thing of the past.” 

• “There is a strong need for constellation of <strong>SAR</strong> satellites at different frequencies 

and different polarization, incidence angle, high repeat cycle of 5 <strong>to</strong> 10 days).” 

• “I would like <strong>to</strong> see an overview of prices of <strong>SAR</strong> data. Should prices be keyed on 

$ per sq km? Or should they be keyed on number of polarizations? Or should they 

be keyed on what the data is used for?” 

• “It is unclear if the desirable extent of <strong>SAR</strong> local / regional coverage could be 

promptly obtained concurrently with an event of interest (i.e. hurricane or flood). 

Apparently, we need <strong>to</strong> learn more about <strong>SAR</strong> capabilities and methods <strong>to</strong> access 



crop losses based on <strong>SAR</strong> images taken before and after a specific event that 

could be detrimental <strong>to</strong> agricultural crops.” 

• “In order <strong>to</strong> make the use of <strong>SAR</strong> data widely operational, investigation on the 

use of <strong>SAR</strong> data should be more emphasized in the universities in terms of 

education and research projects.” 

• “Greater participation of developing countries needs <strong>to</strong> be emphasized.” 

• “The number of researchers working with precision agriculture and remote 

sensing for agriculture in western Canada is very limited. The focus has been 

primarily on optical data and we have a fair bit of expertise with this, but radar is 

attractive because it does not have many of the limitations that optical data has 

such as being affected by atmospheric conditions and colour. The sensitivity of 

<strong>SAR</strong> <strong>to</strong> surface roughness and soil moisture is also of great interest.” 

• “Canadian GEO should include academia. NSERC programs should be available 

through CGEO.” 

• “Much more emphasis in use of SWAT, ecosystem, and other models! More 

importantly, with so many models in use, there is a great need <strong>to</strong> find best/most 

precise methods and re-engineer models <strong>to</strong> utilize EO data best: funding for intercomparison 

studies on SWAT is needed.” 



6. Conclusions 

The survey responses of the sixty-two respondents (the <strong>to</strong>tal received as of early 

December 2009) have been analyzed and reported in their entirety. This information was 

presented by the author at the International GEO Workshop on <strong>SAR</strong> for <strong>Agricultural</strong> 

Moni<strong>to</strong>ring. This information was also used <strong>to</strong> plan the break-out sessions and the panel 

discussion at the end of the workshop. 



Annex A – Questionnaire 

Survey on Synthetic Aperture Radar (<strong>SAR</strong>) <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> 


� In preparation for the upcoming International GEO Workshop on Synthetic 

Aperture Radar (<strong>SAR</strong>) <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring, you are invited <strong>to</strong> 

complete this survey on your views of the use of <strong>SAR</strong> for agricultural applications. 

Information about the Workshop can be found at www.cgeo.gc.ca. 

� The goal of this Workshop is <strong>to</strong> state clearly and strongly the Earth Observation 

requirements of the international agriculture community and the strategies needed 

<strong>to</strong> promote the use of <strong>SAR</strong> for agricultural moni<strong>to</strong>ring, in the areas of science, 

research, operations and capacity. 

� The findings from this survey will be presented at the Workshop and will form the 

starting point for discussions during Workshop breakout sessions. 

� Whether or not you attend the Workshop, this survey provides an important 

opportunity for you <strong>to</strong> express your views on the priority strategies <strong>to</strong> facilitate 

greater use of <strong>SAR</strong> by the agriculture community. A report from the Workshop will 

include a summary of these survey results. This report will be made widely 

available at www.cgeo.gc.ca. 

� The survey response will take approximately 20 minutes, depending on how indepth 

your responses are. 

� Please save your completed survey and return <strong>to</strong> cgeo-survey@ec.gc.ca as a file 

attachment. If you have any questions about the survey, please direct them <strong>to</strong> 

cgeo-survey@ec.gc.ca . 

� The study results will be reported only in the aggregate and your personal 

information will not be divulged. 

� Your participation is important and greatly appreciated. 

Respondent’s Profile 

Last Name 

First Name 

Job Title 

Organization 

Gender Female Male 

Country 

Telephone 

E-mail 

Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring – Annex 1


Type of Organization: International Federal Government 2 

State/Provincial Government 1 University Private sec<strong>to</strong>r 

Not-for-profit Other 

Please describe your organization: 

Please describe your role in your organization: 

Scientist Manager Technician Other (specifiy): 

2 Includes government owned corporations or agencies. 



Section A. Your Current and Planned Use of <strong>Agricultural</strong> Moni<strong>to</strong>ring Data 

1. What are your current needs for agricultural information? Please fill in the 

following table where applicable. 




Crop 


Crop Area 

Crop Health 

Crop Yield 

Other: 


Soil Moisture 




Drought 


Other: 


Land Cover 

Land Use 

Soil Type 

Tillage 


Crop Residue 

Other: 


Habitat Type 

Habitat Diversity 

Habitat 

Connectivity 

Water Erosion 

Wind Erosion 



Other: 

Research 

(mark ‘X’ if 

applicable) 


(mark ‘X’ if 

applicable) 

Geographic 

Location of 

Activity 

Spatial Scale 

Other information that may be helpful <strong>to</strong> understand your needs for agricultural 

information: 

Report on Results: Survey on <strong>SAR</strong> <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring – Annex 1 

Time Scale


2a. What types of <strong>SAR</strong> data do you currently use and do you plan <strong>to</strong> use? Please mark 

‘X’ where applicable. 





Crop Area 

Crop Health 

Crop Yield 

Other: 


Soil Moisture 




Drought 

Other: 


Land Cover 

Land Use 

Soil Type 


Crop Residue 

Other: 


Habitat Type 


Habitat 





Other: 

RADARSA 

T-1 

Currently Use 


RADARSA 

T-2 

Currently Use 


*Other: Please name the sensor(s): 

Enivsat 

A<strong>SAR</strong> 

Currently Use 


Terra<strong>SAR</strong>- 

X 

Currently Use 

ALOS 

PAL<strong>SAR</strong> 

Cosmo- 

Skymed 

RISAT 

Sentinel-1 



Currently Use 


Currently Use 




Currently Use 

Other* 



2b. Approximately how many scenes of <strong>SAR</strong> do you currently use in one year? 

500 

3. What types of optical data do you currently use and do you plan <strong>to</strong> use? Please mark 

‘X’ where applicable. 




Crop 


Crop Area 

Crop Health 

Crop Yield 

Other: 


Soil Moisture 




Drought 


Other: 


Land Cover 

Land Use 

Soil Type 


Crop Residue 

Other: 


Habitat Type 


Habitat 



AVHRR 

MODIS 

MERIS 

AWiFS 

Landsat 

SPOT-4/5 

LISS 

CEBERS 

Rapid-Eye 

IKONOS 


QuickBird 

Other* 

Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 







Other: 


AVHRR 

MODIS 

MERIS 

*Other: Please name the sensor(s): 

AWiFS 

Landsat 

SPOT-4/5 

LISS 

CEBERS 

Rapid-Eye 

IKONOS 


QuickBird 

Other* 

Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


Currently Use 


4. What types of in situ data (i.e. meteorological data, field surveys etc…) do you 

currently use and do you plan <strong>to</strong> use? Please fill in the following table where applicable. 




Crop 


Crop Area 

Crop Health 

Crop Yield 

Other: 


Soil Moisture 




Drought 


In-situ Data Currently Used In-situ Data Plan <strong>to</strong> Use



Other: 


Land Cover 

Land Use 

Soil Type 

Tillage 


Crop Residue 

Other: 


Habitat Type 


Habitat 






Other: 


In-situ Data Currently Used In-situ Data Plan <strong>to</strong> Use 

5a. From the moni<strong>to</strong>ring applications identified in Question 1 through 4, what are the 

information requirements for your two priority applications? 

Moni<strong>to</strong>ring application 1: 

a. Product accuracy: 

b. Spatial resolution/mapping unit: 

c. Frequency of information delivery/reporting: 

d. Timeliness of information delivery/reporting 

e. Other information requirements: 


a. Product accuracy: 



b. Spatial resolution/mapping unit: 

c. Frequency of information delivery/reporting: 

d. Timeliness of information delivery/reporting 

e. Other information requirements: 

5b. At what stage are your moni<strong>to</strong>ring applications? Please select one option only for 

each moni<strong>to</strong>ring application. 


Research Pilot / Demonstration Operational 


Research Pilot / Demonstration Operational 

5c. How important is the use of <strong>SAR</strong> data for your agricultural moni<strong>to</strong>ring applications? 

Please select one option only for each moni<strong>to</strong>ring application. 


Not Important Somewhat Important Increasingly Important Critical 


Not Important Somewhat Important Increasingly Important Critical 



Section B. Your Views on How <strong>to</strong> Encourage Greater Use of <strong>SAR</strong> Data 

6. What fac<strong>to</strong>rs would encourage the greater use of <strong>SAR</strong> data for your agricultural 

moni<strong>to</strong>ring needs? (Rate each from 1 <strong>to</strong> 5 by placing ‘X’, with 1 being least important 

and 5 being most important) 

• Improved spatial (swath) coverage 

• Higher spatial (pixel) resolution 

• Desired <strong>SAR</strong> frequency (wavelength) available 

• Desired <strong>SAR</strong> polarization available 

• Improved timeliness of satellite data access 

• Improved revisit/re-look of satellite passes 

• Improved availability of models or methods <strong>to</strong> 

derive information from data 

• Greater access <strong>to</strong> new acquisitions 

• Greater access <strong>to</strong> his<strong>to</strong>rical data archives 

• Improved image quality and calibration 

• Reduced data cost 

• Improved data policies (e.g. data sharing) 

• Improved knowledge <strong>to</strong> use <strong>SAR</strong> data through 

training 

• Greater availability of expert personnel 

• Greater availability of software <strong>to</strong>ols and 

applications 

• Reduce gap between research & operational 

users 

• Greater access <strong>to</strong> application specific training 

• Improved data interoperability or integration 

• Access <strong>to</strong> geometrically corrected or ready-<strong>to</strong>use 

data 

• Easier data discovery and order process (e.g. 

global portal) 

• Policy of commitment <strong>to</strong> data continuity 

Please feel free <strong>to</strong> comment further on other fac<strong>to</strong>rs: 

Least 

Important 

(1) 

2 3 4 


Most 

Important 

(5)


7. What suggestions do you have <strong>to</strong> encourage greater use of <strong>SAR</strong> data through 

science (rate each from 1 <strong>to</strong> 5 by placing ‘X’, with 1 being least important and 5 

being most important)? 

• Enhance scientific exchanges through 

annual/bi-annual agriculture Workshops 

• Develop an agricultural <strong>SAR</strong> data 

community of practice 

• Target special journal and book 

publications <strong>to</strong> disseminate state of the art 

science, specific for agriculture 

• Create a <strong>SAR</strong> training manual for 

agriculture 

• Establish international super sites <strong>to</strong> 

facilitate research collaborations 

• Develop an inven<strong>to</strong>ry of international 

science activities and <strong>SAR</strong> experts 

• Establish demonstration projects around 

agricultural themes which include both the 

science and user communities 

• Encourage data providers <strong>to</strong> provide 

access <strong>to</strong> new and archived data for 

research projects, at no or minimal cost 

• Through the GEO web site, provide links <strong>to</strong> 

national and international announcements 

of opportunities and other funding 

opportunities 

• Establish a website portal that includes 

training on <strong>SAR</strong> methods and best 

practices for agriculture applications, 

glossary of terms, list of experts, user 

forums and links <strong>to</strong> <strong>SAR</strong> data providers and 

other resources 

• Use of online <strong>to</strong>ols <strong>to</strong> disseminate 

information 

Please feel free <strong>to</strong> provide other suggestions: 

Least 

Important 

(1) 

2 3 4 


Most 

Important 

(5)


8. What suggestions do you have <strong>to</strong> encourage greater operational use of <strong>SAR</strong> 

data (rate each from 1 <strong>to</strong> 5 by placing ‘X’, with 1 being least important and 5 being 

most important)? 

• Coordinate data procurement <strong>to</strong> take 

advantage of economies of scale 

• Develop a consensus on data policy needs 

for agriculture and design a strategy <strong>to</strong> 

negotiate liberal data policies 

• Influence existing and new <strong>SAR</strong> sensor 

development by documenting the 

requirements of the agriculture community 

with respect <strong>to</strong>: 

� Spatial resolution 

� Spatial coverage 

� Temporal revisit/re-look 

� Data latency/delivery 

� <strong>SAR</strong> sensor parameters (frequency, 

polarizations, angles) 

• Encourage international space agencies <strong>to</strong> 

consider data exchanges <strong>to</strong> meet the needs 

of the agriculture community 

• Document best practices or approaches <strong>to</strong> 

designing area sample frameworks, data 

pre-processing and processing, methods 

and accuracy assessments related <strong>to</strong> <strong>SAR</strong> 

and agriculture 

• Create an international team <strong>to</strong> assess 


design approaches <strong>to</strong> inter-calibrate among 

sensors 

• Encourage the creation of <strong>SAR</strong> satellite 

constellations <strong>to</strong> improve data continuity, 

revisit, provide consistency and prevent 

redundancy 


Least 

Important 

(1) 

2 3 4 


Most 

Important 

(5)


9. What suggestions do you have <strong>to</strong> encourage greater capacity <strong>to</strong> use <strong>SAR</strong> data 

(rate each from 1 <strong>to</strong> 5 by placing ‘X’, with 1 being least important and 5 being most 

important)? 

• Develop, disseminate and update <strong>SAR</strong> 

training courses and technical manuals 

specifically designed for the agriculture 

community 

• Through the GEO website, advertise 

opportunities for internships and 

exchanges with leading <strong>SAR</strong> institutions 

• Encourage <strong>SAR</strong> data providers <strong>to</strong> 

provide sample data for educational and 

research purposes so that new users 

can practice what they have learned 

• Develop lecture series on <strong>SAR</strong> and 

agriculture which could be included in 

university and college courses 

• Encourage <strong>SAR</strong> software developers <strong>to</strong> 

provide software for educational 

purposes at minimal or no cost 

• Through the GEO <strong>Agricultural</strong> Moni<strong>to</strong>ring 

Community of Practice, build a ‘network 

of networks’ in <strong>SAR</strong> and agriculture <strong>to</strong> 

coordinate/share resources and 

experiences 


Community of Practice, compile list of 

potential avenues for obtaining funds for 

infrastructure development (computers, 

software, Internet, etc). <strong>to</strong> facilitate 

greater use of Earth Observation data 

and capacity building opportunities 


Community of Practice, develop user 

forums <strong>to</strong> drive capacity needs 


Least 

Important 

(1) 

2 3 4 


Most 

Important 

(5)


10. What other suggestions do you have <strong>to</strong> encourage greater use of <strong>SAR</strong> for 

agricultural moni<strong>to</strong>ring? 

11. Do you have other views that are relevant <strong>to</strong> the upcoming International GEO 

Workshop on Synthetic Aperture Radar <strong>to</strong> <strong>Support</strong> <strong>Agricultural</strong> Moni<strong>to</strong>ring, but 

are not covered by the survey questions? 

__________ 

Thank you for your time and contribution <strong>to</strong> this study. 



Annex B - Acronyms 

AAFC Agriculture and Agri-Food Canada 

CCRS Canada Centre for Remote Sensing 

CFS Canadian Forest Service 

C-GEO Canadian <strong>Group</strong> on Earth Observations 

CSA Canadian Space Agency 

EOSD Earth Observation for the Sustainable Development of Forests 

GOFC-GOLD Global Observation of Forest and Land Cover Dynamics 

GRIP Government Related Initiatives Program 

IACG Inter Agency Committee on Geomatics 

ISO International Organization for Standardization 

LAI Leaf Area Index 

LCCoP Land Cover Community of Practice 

NLWIS National Land and Water Information Service 

NRCan Natural Resources Canada 

NRT near real-time 

PFRA Prairie Farm Rehabilitation Administration

(SAR) to Support Agricultural Monitoring 2 - Group on Earth ...

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